Overview

Brought to you by YData

Dataset statistics

Number of variables45
Number of observations17759
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.7 MiB
Average record size in memory983.9 B

Variable types

Numeric22
Text7
Unsupported1
Categorical13
DateTime2

Alerts

code_status has constant value "Clear" Constant
country has constant value "India" Constant
iso3 has constant value "IND" Constant
country_id has constant value "750" Constant
region has constant value "Asia" Constant
conflict_name has a high cardinality: 61 distinct values High cardinality
dyad_name has a high cardinality: 72 distinct values High cardinality
adm_1 is highly overall correlated with latitude and 2 other fieldsHigh correlation
best is highly overall correlated with high and 1 other fieldsHigh correlation
conflict_dset_id is highly overall correlated with conflict_name and 6 other fieldsHigh correlation
conflict_name is highly overall correlated with conflict_dset_id and 14 other fieldsHigh correlation
conflict_new_id is highly overall correlated with conflict_name and 9 other fieldsHigh correlation
date_prec is highly overall correlated with event_clarityHigh correlation
deaths_b is highly overall correlated with deaths_civilians and 3 other fieldsHigh correlation
deaths_civilians is highly overall correlated with conflict_new_id and 7 other fieldsHigh correlation
dyad_dset_id is highly overall correlated with conflict_dset_id and 7 other fieldsHigh correlation
dyad_name is highly overall correlated with conflict_dset_id and 14 other fieldsHigh correlation
dyad_new_id is highly overall correlated with conflict_name and 8 other fieldsHigh correlation
event_clarity is highly overall correlated with date_precHigh correlation
high is highly overall correlated with best and 1 other fieldsHigh correlation
latitude is highly overall correlated with adm_1 and 4 other fieldsHigh correlation
longitude is highly overall correlated with adm_1 and 4 other fieldsHigh correlation
low is highly overall correlated with best and 1 other fieldsHigh correlation
priogrid_gid is highly overall correlated with adm_1 and 4 other fieldsHigh correlation
side_a is highly overall correlated with conflict_dset_id and 11 other fieldsHigh correlation
side_a_dset_id is highly overall correlated with conflict_name and 10 other fieldsHigh correlation
side_a_new_id is highly overall correlated with conflict_name and 10 other fieldsHigh correlation
side_b is highly overall correlated with conflict_dset_id and 11 other fieldsHigh correlation
side_b_dset_id is highly overall correlated with conflict_name and 9 other fieldsHigh correlation
side_b_new_id is highly overall correlated with conflict_dset_id and 9 other fieldsHigh correlation
type_of_violence is highly overall correlated with conflict_dset_id and 9 other fieldsHigh correlation
side_a is highly imbalanced (66.7%) Imbalance
event_clarity is highly imbalanced (77.6%) Imbalance
date_prec is highly imbalanced (73.8%) Imbalance
best is highly skewed (γ1 = 61.86834051) Skewed
high is highly skewed (γ1 = 56.21144768) Skewed
low is highly skewed (γ1 = 62.75181918) Skewed
id has unique values Unique
relid has unique values Unique
year is an unsupported type, check if it needs cleaning or further analysis Unsupported
deaths_a has 13506 (76.1%) zeros Zeros
deaths_b has 9489 (53.4%) zeros Zeros
deaths_civilians has 12408 (69.9%) zeros Zeros
deaths_unknown has 17127 (96.4%) zeros Zeros
best has 1049 (5.9%) zeros Zeros
low has 1463 (8.2%) zeros Zeros

Reproduction

Analysis started2025-04-14 14:56:29.279461
Analysis finished2025-04-14 14:57:53.757687
Duration1 minute and 24.48 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Unique 

Distinct17759
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148613.11
Minimum48692
Maximum513563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:53.872191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum48692
5-th percentile73118.9
Q187276
median92855
Q3143841.5
95-th percentile449327.4
Maximum513563
Range464871
Interquartile range (IQR)56565.5

Descriptive statistics

Standard deviation114840.28
Coefficient of variation (CV)0.77274664
Kurtosis1.7615611
Mean148613.11
Median Absolute Deviation (MAD)6365
Skewness1.7660282
Sum2.6392203 × 109
Variance1.3188291 × 1010
MonotonicityNot monotonic
2025-04-14T14:57:53.992020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95595 1
 
< 0.1%
71629 1
 
< 0.1%
71630 1
 
< 0.1%
59216 1
 
< 0.1%
59218 1
 
< 0.1%
59247 1
 
< 0.1%
59413 1
 
< 0.1%
59506 1
 
< 0.1%
372975 1
 
< 0.1%
54312 1
 
< 0.1%
Other values (17749) 17749
99.9%
ValueCountFrequency (%)
48692 1
< 0.1%
51770 1
< 0.1%
51771 1
< 0.1%
51775 1
< 0.1%
52043 1
< 0.1%
52167 1
< 0.1%
52172 1
< 0.1%
52194 1
< 0.1%
52201 1
< 0.1%
52247 1
< 0.1%
ValueCountFrequency (%)
513563 1
< 0.1%
512138 1
< 0.1%
512136 1
< 0.1%
510017 1
< 0.1%
507351 1
< 0.1%
507349 1
< 0.1%
507347 1
< 0.1%
507331 1
< 0.1%
507321 1
< 0.1%
507318 1
< 0.1%

relid
Text

Unique 

Distinct17759
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-04-14T14:57:54.230585image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length21
Median length20
Mean length17.436849
Min length16

Characters and Unicode

Total characters309661
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17759 ?
Unique (%)100.0%

Sample

1st rowIND-1989-1-345-0
2nd rowIND-1990-1-345-0
3rd rowPAK-1990-1-345-1
4th rowPAK-1990-1-345-2
5th rowPAK-1990-1-345-3
ValueCountFrequency (%)
ind-1992-1-345-0 1
 
< 0.1%
ind-1993-2-15181-2 1
 
< 0.1%
ind-1989-1-345-0 1
 
< 0.1%
ind-1990-1-345-0 1
 
< 0.1%
pak-1990-1-345-1 1
 
< 0.1%
pak-1990-1-345-2 1
 
< 0.1%
pak-1990-1-345-3 1
 
< 0.1%
pak-1990-1-345-4 1
 
< 0.1%
pak-1991-1-345-3 1
 
< 0.1%
ind-1991-1-422-1 1
 
< 0.1%
Other values (17749) 17749
99.9%
2025-04-14T14:57:54.504803image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 71036
22.9%
1 37303
12.0%
0 32050
10.4%
2 27843
 
9.0%
3 20081
 
6.5%
5 19752
 
6.4%
N 17605
 
5.7%
I 17601
 
5.7%
D 17601
 
5.7%
9 14153
 
4.6%
Other values (16) 34636
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 184656
59.6%
Dash Punctuation 71036
 
22.9%
Uppercase Letter 53328
 
17.2%
Other Punctuation 641
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 17605
33.0%
I 17601
33.0%
D 17601
33.0%
A 154
 
0.3%
P 151
 
0.3%
K 151
 
0.3%
X 51
 
0.1%
G 3
 
< 0.1%
H 2
 
< 0.1%
C 2
 
< 0.1%
Other values (4) 7
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 37303
20.2%
0 32050
17.4%
2 27843
15.1%
3 20081
10.9%
5 19752
10.7%
9 14153
 
7.7%
4 9262
 
5.0%
6 9230
 
5.0%
7 7941
 
4.3%
8 7041
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 71036
100.0%
Other Punctuation
ValueCountFrequency (%)
. 641
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 256333
82.8%
Latin 53328
 
17.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 17605
33.0%
I 17601
33.0%
D 17601
33.0%
A 154
 
0.3%
P 151
 
0.3%
K 151
 
0.3%
X 51
 
0.1%
G 3
 
< 0.1%
H 2
 
< 0.1%
C 2
 
< 0.1%
Other values (4) 7
 
< 0.1%
Common
ValueCountFrequency (%)
- 71036
27.7%
1 37303
14.6%
0 32050
12.5%
2 27843
 
10.9%
3 20081
 
7.8%
5 19752
 
7.7%
9 14153
 
5.5%
4 9262
 
3.6%
6 9230
 
3.6%
7 7941
 
3.1%
Other values (2) 7682
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 309661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 71036
22.9%
1 37303
12.0%
0 32050
10.4%
2 27843
 
9.0%
3 20081
 
6.5%
5 19752
 
6.4%
N 17605
 
5.7%
I 17601
 
5.7%
D 17601
 
5.7%
9 14153
 
4.6%
Other values (16) 34636
11.2%

year
Unsupported

Rejected  Unsupported 

Missing0
Missing (%)0.0%
Memory size1.1 MiB

active_year
Real number (ℝ)

Distinct35
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006.0234
Minimum1989
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:54.608946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1992
Q12000
median2005
Q32011
95-th percentile2020
Maximum2023
Range34
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.9812761
Coefficient of variation (CV)0.0039786555
Kurtosis-0.51460214
Mean2006.0234
Median Absolute Deviation (MAD)5
Skewness0.14741424
Sum35624970
Variance63.700769
MonotonicityNot monotonic
2025-04-14T14:57:54.713955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2000 1534
 
8.6%
2002 1083
 
6.1%
2004 997
 
5.6%
2006 932
 
5.2%
2003 919
 
5.2%
2005 910
 
5.1%
2009 843
 
4.7%
2010 780
 
4.4%
2007 727
 
4.1%
2008 680
 
3.8%
Other values (25) 8354
47.0%
ValueCountFrequency (%)
1989 119
 
0.7%
1990 333
1.9%
1991 221
1.2%
1992 240
1.4%
1993 331
1.9%
1994 244
1.4%
1995 226
1.3%
1996 177
1.0%
1997 277
1.6%
1998 386
2.2%
ValueCountFrequency (%)
2023 214
1.2%
2022 266
1.5%
2021 277
1.6%
2020 389
2.2%
2019 354
2.0%
2018 479
2.7%
2017 400
2.3%
2016 419
2.4%
2015 321
1.8%
2014 400
2.3%

code_status
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
Clear
17759 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters88795
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowClear
2nd rowClear
3rd rowClear
4th rowClear
5th rowClear

Common Values

ValueCountFrequency (%)
Clear 17759
100.0%

Length

2025-04-14T14:57:54.832163image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:57:54.902630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
clear 17759
100.0%

Most occurring characters

ValueCountFrequency (%)
C 17759
20.0%
l 17759
20.0%
e 17759
20.0%
a 17759
20.0%
r 17759
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71036
80.0%
Uppercase Letter 17759
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 17759
25.0%
e 17759
25.0%
a 17759
25.0%
r 17759
25.0%
Uppercase Letter
ValueCountFrequency (%)
C 17759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88795
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 17759
20.0%
l 17759
20.0%
e 17759
20.0%
a 17759
20.0%
r 17759
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 17759
20.0%
l 17759
20.0%
e 17759
20.0%
a 17759
20.0%
r 17759
20.0%

type_of_violence
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1.0
12270 
3.0
4825 
2.0
 
664

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters53277
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 12270
69.1%
3.0 4825
 
27.2%
2.0 664
 
3.7%

Length

2025-04-14T14:57:54.972718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:57:55.049446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 12270
69.1%
3.0 4825
 
27.2%
2.0 664
 
3.7%

Most occurring characters

ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 12270
23.0%
3 4825
 
9.1%
2 664
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35518
66.7%
Other Punctuation 17759
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17759
50.0%
1 12270
34.5%
3 4825
 
13.6%
2 664
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 17759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 12270
23.0%
3 4825
 
9.1%
2 664
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 12270
23.0%
3 4825
 
9.1%
2 664
 
1.2%

conflict_dset_id
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean728.19123
Minimum141
Maximum15181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:55.143074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile195
Q1227
median364
Q3364
95-th percentile5289
Maximum15181
Range15040
Interquartile range (IQR)137

Descriptive statistics

Standard deviation1911.1417
Coefficient of variation (CV)2.6245053
Kurtosis27.737404
Mean728.19123
Median Absolute Deviation (MAD)38
Skewness5.187682
Sum12931948
Variance3652462.7
MonotonicityNot monotonic
2025-04-14T14:57:55.292169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
364 7173
40.4%
227 2344
 
13.2%
325 1842
 
10.4%
195 1629
 
9.2%
218 637
 
3.6%
365 526
 
3.0%
347 424
 
2.4%
421 377
 
2.1%
5512 245
 
1.4%
319 227
 
1.3%
Other values (51) 2335
 
13.1%
ValueCountFrequency (%)
141 137
 
0.8%
143 1
 
< 0.1%
193 53
 
0.3%
194 45
 
0.3%
195 1629
9.2%
218 637
 
3.6%
222 1
 
< 0.1%
223 55
 
0.3%
227 2344
13.2%
234 1
 
< 0.1%
ValueCountFrequency (%)
15181 3
 
< 0.1%
13653 139
0.8%
12228 27
 
0.2%
12175 14
 
0.1%
12004 21
 
0.1%
11947 7
 
< 0.1%
11475 1
 
< 0.1%
11390 28
 
0.2%
11342 76
0.4%
11286 2
 
< 0.1%

conflict_new_id
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean787.95889
Minimum218
Maximum14003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:55.420354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum218
5-th percentile227
Q1364
median364
Q3499
95-th percentile4679
Maximum14003
Range13785
Interquartile range (IQR)135

Descriptive statistics

Standard deviation1912.8514
Coefficient of variation (CV)2.4276031
Kurtosis28.850751
Mean787.95889
Median Absolute Deviation (MAD)57
Skewness5.3093754
Sum13993362
Variance3659000.6
MonotonicityNot monotonic
2025-04-14T14:57:55.540636image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
364 7173
40.4%
227 2344
 
13.2%
522 1842
 
10.4%
499 1629
 
9.2%
218 637
 
3.6%
365 526
 
3.0%
347 424
 
2.4%
421 377
 
2.1%
4902 245
 
1.4%
519 227
 
1.3%
Other values (51) 2335
 
13.1%
ValueCountFrequency (%)
218 637
 
3.6%
222 1
 
< 0.1%
227 2344
 
13.2%
251 114
 
0.6%
274 2
 
< 0.1%
335 196
 
1.1%
347 424
 
2.4%
351 214
 
1.2%
364 7173
40.4%
365 526
 
3.0%
ValueCountFrequency (%)
14003 3
 
< 0.1%
13653 139
0.8%
13230 42
 
0.2%
13005 1
 
< 0.1%
11576 27
 
0.2%
11523 14
 
0.1%
11475 1
 
< 0.1%
11361 21
 
0.1%
11342 76
0.4%
11318 7
 
< 0.1%

conflict_name
Categorical

High cardinality  High correlation 

Distinct61
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size162.7 KiB
India: Kashmir
7173 
India: Government
2344 
Kashmir insurgents - Civilians
1842 
CPI-Maoist - Civilians
1629 
India - Pakistan
 
637
Other values (56)
4134 

Length

Max length59
Median length36
Mean length17.890309
Min length11

Characters and Unicode

Total characters317714
Distinct characters51
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowIndia - Pakistan
2nd rowIndia - Pakistan
3rd rowIndia - Pakistan
4th rowIndia - Pakistan
5th rowIndia - Pakistan

Common Values

ValueCountFrequency (%)
India: Kashmir 7173
40.4%
India: Government 2344
 
13.2%
Kashmir insurgents - Civilians 1842
 
10.4%
CPI-Maoist - Civilians 1629
 
9.2%
India - Pakistan 637
 
3.6%
India: Assam 526
 
3.0%
India: Manipur 424
 
2.4%
India: Bodoland 377
 
2.1%
Hindus (India) - Muslims (India) 245
 
1.4%
Sikh insurgents - Civilians 227
 
1.3%
Other values (51) 2335
 
13.1%

Length

2025-04-14T14:57:55.678182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india 12895
28.5%
kashmir 9015
19.9%
6169
13.7%
civilians 4825
 
10.7%
government 2483
 
5.5%
insurgents 2069
 
4.6%
cpi-maoist 1629
 
3.6%
pakistan 637
 
1.4%
assam 526
 
1.2%
manipur 424
 
0.9%
Other values (68) 4517
 
10.0%

Most occurring characters

ValueCountFrequency (%)
i 42927
13.5%
a 33196
 
10.4%
n 29490
 
9.3%
27453
 
8.6%
s 22890
 
7.2%
I 14939
 
4.7%
r 14794
 
4.7%
d 14204
 
4.5%
m 12523
 
3.9%
: 11631
 
3.7%
Other values (41) 93667
29.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 222055
69.9%
Uppercase Letter 46920
 
14.8%
Space Separator 27453
 
8.6%
Other Punctuation 11845
 
3.7%
Dash Punctuation 8457
 
2.7%
Close Punctuation 492
 
0.2%
Open Punctuation 492
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 42927
19.3%
a 33196
14.9%
n 29490
13.3%
s 22890
10.3%
r 14794
 
6.7%
d 14204
 
6.4%
m 12523
 
5.6%
h 9698
 
4.4%
t 7678
 
3.5%
e 7640
 
3.4%
Other values (13) 27015
12.2%
Uppercase Letter
ValueCountFrequency (%)
I 14939
31.8%
K 9612
20.5%
C 7110
15.2%
P 2700
 
5.8%
M 2647
 
5.6%
G 2619
 
5.6%
N 1520
 
3.2%
S 1035
 
2.2%
A 976
 
2.1%
B 704
 
1.5%
Other values (12) 3058
 
6.5%
Other Punctuation
ValueCountFrequency (%)
: 11631
98.2%
/ 214
 
1.8%
Space Separator
ValueCountFrequency (%)
27453
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8457
100.0%
Close Punctuation
ValueCountFrequency (%)
) 492
100.0%
Open Punctuation
ValueCountFrequency (%)
( 492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 268975
84.7%
Common 48739
 
15.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 42927
16.0%
a 33196
12.3%
n 29490
11.0%
s 22890
 
8.5%
I 14939
 
5.6%
r 14794
 
5.5%
d 14204
 
5.3%
m 12523
 
4.7%
h 9698
 
3.6%
K 9612
 
3.6%
Other values (35) 64702
24.1%
Common
ValueCountFrequency (%)
27453
56.3%
: 11631
23.9%
- 8457
 
17.4%
) 492
 
1.0%
( 492
 
1.0%
/ 214
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 317714
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 42927
13.5%
a 33196
 
10.4%
n 29490
 
9.3%
27453
 
8.6%
s 22890
 
7.2%
I 14939
 
4.7%
r 14794
 
4.7%
d 14204
 
4.5%
m 12523
 
3.9%
: 11631
 
3.7%
Other values (41) 93667
29.5%

dyad_dset_id
Real number (ℝ)

High correlation 

Distinct70
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1066.3109
Minimum141
Maximum15181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:55.786218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile195
Q1363
median792
Q3792
95-th percentile5289
Maximum15181
Range15040
Interquartile range (IQR)429

Descriptive statistics

Standard deviation2148.9081
Coefficient of variation (CV)2.0152734
Kurtosis24.038767
Mean1066.3109
Median Absolute Deviation (MAD)109
Skewness4.8496857
Sum18936616
Variance4617805.9
MonotonicityNot monotonic
2025-04-14T14:57:55.915653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792 7173
40.4%
453 2000
 
11.3%
325 1842
 
10.4%
195 1629
 
9.2%
422 637
 
3.6%
793 526
 
3.0%
451 262
 
1.5%
5512 245
 
1.4%
319 227
 
1.3%
775 214
 
1.2%
Other values (60) 3004
16.9%
ValueCountFrequency (%)
141 137
 
0.8%
143 1
 
< 0.1%
193 53
 
0.3%
194 45
 
0.3%
195 1629
9.2%
223 55
 
0.3%
234 1
 
< 0.1%
306 61
 
0.3%
307 132
 
0.7%
319 227
 
1.3%
ValueCountFrequency (%)
15181 3
 
< 0.1%
14685 139
0.8%
14102 59
0.3%
12228 27
 
0.2%
12175 14
 
0.1%
12004 21
 
0.1%
11971 76
0.4%
11947 7
 
< 0.1%
11932 20
 
0.1%
11390 28
 
0.2%

dyad_new_id
Real number (ℝ)

High correlation 

Distinct72
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1277.0482
Minimum422
Maximum15181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:56.045833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum422
5-th percentile451
Q1792
median792
Q3966
95-th percentile5289
Maximum15181
Range14759
Interquartile range (IQR)174

Descriptive statistics

Standard deviation2185.2208
Coefficient of variation (CV)1.7111499
Kurtosis23.366798
Mean1277.0482
Median Absolute Deviation (MAD)162
Skewness4.823511
Sum22679099
Variance4775190.1
MonotonicityIncreasing
2025-04-14T14:57:56.172580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792 7173
40.4%
453 2000
 
11.3%
989 1842
 
10.4%
966 1629
 
9.2%
422 637
 
3.6%
793 526
 
3.0%
451 262
 
1.5%
5512 245
 
1.4%
986 227
 
1.3%
775 214
 
1.2%
Other values (62) 3004
16.9%
ValueCountFrequency (%)
422 637
 
3.6%
451 262
 
1.5%
452 62
 
0.3%
453 2000
11.3%
508 114
 
0.6%
569 2
 
< 0.1%
739 57
 
0.3%
740 114
 
0.6%
768 130
 
0.7%
769 142
 
0.8%
ValueCountFrequency (%)
15181 3
 
< 0.1%
14685 139
0.8%
14102 59
0.3%
14097 42
 
0.2%
13782 1
 
< 0.1%
12228 27
 
0.2%
12175 14
 
0.1%
12004 21
 
0.1%
11971 76
0.4%
11947 7
 
< 0.1%

dyad_name
Categorical

High cardinality  High correlation 

Distinct72
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size163.8 KiB
Government of India - Kashmir insurgents
7173 
Government of India - CPI-Maoist
2000 
Kashmir insurgents - Civilians
1842 
CPI-Maoist - Civilians
1629 
Government of India - Government of Pakistan
 
637
Other values (67)
4478 

Length

Max length59
Median length44
Mean length32.650374
Min length11

Characters and Unicode

Total characters579838
Distinct characters48
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowGovernment of India - Government of Pakistan
2nd rowGovernment of India - Government of Pakistan
3rd rowGovernment of India - Government of Pakistan
4th rowGovernment of India - Government of Pakistan
5th rowGovernment of India - Government of Pakistan

Common Values

ValueCountFrequency (%)
Government of India - Kashmir insurgents 7173
40.4%
Government of India - CPI-Maoist 2000
 
11.3%
Kashmir insurgents - Civilians 1842
 
10.4%
CPI-Maoist - Civilians 1629
 
9.2%
Government of India - Government of Pakistan 637
 
3.6%
Government of India - ULFA 526
 
3.0%
Government of India - PWG 262
 
1.5%
Hindus (India) - Muslims (India) 245
 
1.4%
Sikh insurgents - Civilians 227
 
1.3%
Government of India - Sikh insurgents 214
 
1.2%
Other values (62) 3004
16.9%

Length

2025-04-14T14:57:56.305055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17903
19.9%
government 13047
14.5%
of 13047
14.5%
india 12895
14.4%
insurgents 9456
10.5%
kashmir 9015
10.0%
civilians 4825
 
5.4%
cpi-maoist 3629
 
4.0%
ulfa 728
 
0.8%
pakistan 637
 
0.7%
Other values (66) 4586
 
5.1%

Most occurring characters

ValueCountFrequency (%)
72032
12.4%
n 63833
 
11.0%
i 51509
 
8.9%
s 37935
 
6.5%
e 35831
 
6.2%
a 32164
 
5.5%
r 31714
 
5.5%
o 29900
 
5.2%
t 26906
 
4.6%
m 22515
 
3.9%
Other values (38) 175499
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 412195
71.1%
Uppercase Letter 72197
 
12.5%
Space Separator 72032
 
12.4%
Dash Punctuation 22430
 
3.9%
Open Punctuation 492
 
0.1%
Close Punctuation 492
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 63833
15.5%
i 51509
12.5%
s 37935
9.2%
e 35831
8.7%
a 32164
7.8%
r 31714
7.7%
o 29900
7.3%
t 26906
6.5%
m 22515
 
5.5%
v 17884
 
4.3%
Other values (12) 62004
15.0%
Uppercase Letter
ValueCountFrequency (%)
I 17027
23.6%
G 13445
18.6%
K 9551
13.2%
C 9448
13.1%
P 5170
 
7.2%
M 4419
 
6.1%
N 2496
 
3.5%
F 2089
 
2.9%
L 1665
 
2.3%
S 1251
 
1.7%
Other values (12) 5636
 
7.8%
Space Separator
ValueCountFrequency (%)
72032
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22430
100.0%
Open Punctuation
ValueCountFrequency (%)
( 492
100.0%
Close Punctuation
ValueCountFrequency (%)
) 492
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 484392
83.5%
Common 95446
 
16.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 63833
13.2%
i 51509
 
10.6%
s 37935
 
7.8%
e 35831
 
7.4%
a 32164
 
6.6%
r 31714
 
6.5%
o 29900
 
6.2%
t 26906
 
5.6%
m 22515
 
4.6%
v 17884
 
3.7%
Other values (34) 134201
27.7%
Common
ValueCountFrequency (%)
72032
75.5%
- 22430
 
23.5%
( 492
 
0.5%
) 492
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 579838
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
72032
12.4%
n 63833
 
11.0%
i 51509
 
8.9%
s 37935
 
6.5%
e 35831
 
6.2%
a 32164
 
5.5%
r 31714
 
5.5%
o 29900
 
5.2%
t 26906
 
4.6%
m 22515
 
3.9%
Other values (38) 175499
30.3%

side_a_dset_id
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.79706
Minimum135
Maximum5826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:56.418664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile141
Q1141
median141
Q3195
95-th percentile326
Maximum5826
Range5691
Interquartile range (IQR)54

Descriptive statistics

Standard deviation372.50768
Coefficient of variation (CV)1.6570843
Kurtosis144.99255
Mean224.79706
Median Absolute Deviation (MAD)0
Skewness11.106284
Sum3992171
Variance138761.97
MonotonicityNot monotonic
2025-04-14T14:57:56.556377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
141 12403
69.8%
325 1842
 
10.4%
195 1629
 
9.2%
858 245
 
1.4%
223 239
 
1.3%
319 227
 
1.3%
326 202
 
1.1%
307 132
 
0.7%
363 124
 
0.7%
1187 79
 
0.4%
Other values (33) 637
 
3.6%
ValueCountFrequency (%)
135 2
 
< 0.1%
141 12403
69.8%
143 1
 
< 0.1%
144 2
 
< 0.1%
193 53
 
0.3%
194 45
 
0.3%
195 1629
 
9.2%
223 239
 
1.3%
224 43
 
0.2%
234 1
 
< 0.1%
ValueCountFrequency (%)
5826 42
0.2%
5546 1
 
< 0.1%
3963 21
 
0.1%
3955 7
 
< 0.1%
3057 28
 
0.2%
2922 16
 
0.1%
2907 3
 
< 0.1%
2707 1
 
< 0.1%
1187 79
0.4%
1185 4
 
< 0.1%

side_a_new_id
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean224.79706
Minimum135
Maximum5826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:56.688337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum135
5-th percentile141
Q1141
median141
Q3195
95-th percentile326
Maximum5826
Range5691
Interquartile range (IQR)54

Descriptive statistics

Standard deviation372.50768
Coefficient of variation (CV)1.6570843
Kurtosis144.99255
Mean224.79706
Median Absolute Deviation (MAD)0
Skewness11.106284
Sum3992171
Variance138761.97
MonotonicityNot monotonic
2025-04-14T14:57:56.812203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
141 12403
69.8%
325 1842
 
10.4%
195 1629
 
9.2%
858 245
 
1.4%
223 239
 
1.3%
319 227
 
1.3%
326 202
 
1.1%
307 132
 
0.7%
363 124
 
0.7%
1187 79
 
0.4%
Other values (33) 637
 
3.6%
ValueCountFrequency (%)
135 2
 
< 0.1%
141 12403
69.8%
143 1
 
< 0.1%
144 2
 
< 0.1%
193 53
 
0.3%
194 45
 
0.3%
195 1629
 
9.2%
223 239
 
1.3%
224 43
 
0.2%
234 1
 
< 0.1%
ValueCountFrequency (%)
5826 42
0.2%
5546 1
 
< 0.1%
3963 21
 
0.1%
3955 7
 
< 0.1%
3057 28
 
0.2%
2922 16
 
0.1%
2907 3
 
< 0.1%
2707 1
 
< 0.1%
1187 79
0.4%
1185 4
 
< 0.1%

side_a
Categorical

High correlation  Imbalance 

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size159.9 KiB
Government of India
12403 
Kashmir insurgents
1842 
CPI-Maoist
1629 
Hindus (India)
 
245
NSCN-IM
 
239
Other values (38)
1401 

Length

Max length30
Median length19
Mean length16.907484
Min length2

Characters and Unicode

Total characters300260
Distinct characters46
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowGovernment of India
2nd rowGovernment of India
3rd rowGovernment of India
4th rowGovernment of India
5th rowGovernment of India

Common Values

ValueCountFrequency (%)
Government of India 12403
69.8%
Kashmir insurgents 1842
 
10.4%
CPI-Maoist 1629
 
9.2%
Hindus (India) 245
 
1.4%
NSCN-IM 239
 
1.3%
Sikh insurgents 227
 
1.3%
ULFA 202
 
1.1%
NLFT 132
 
0.7%
NDFB 124
 
0.7%
PLFI 79
 
0.4%
Other values (33) 637
 
3.6%

Length

2025-04-14T14:57:56.953055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
india 12648
28.1%
government 12408
27.5%
of 12408
27.5%
insurgents 2069
 
4.6%
kashmir 1842
 
4.1%
cpi-maoist 1629
 
3.6%
hindus 245
 
0.5%
nscn-im 239
 
0.5%
sikh 227
 
0.5%
ulfa 202
 
0.4%
Other values (43) 1152
 
2.6%

Most occurring characters

ValueCountFrequency (%)
n 42021
14.0%
27310
 
9.1%
e 27035
 
9.0%
o 26541
 
8.8%
i 18955
 
6.3%
r 16438
 
5.5%
a 16423
 
5.5%
t 16158
 
5.4%
I 14646
 
4.9%
m 14393
 
4.8%
Other values (36) 80340
26.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 230933
76.9%
Uppercase Letter 39464
 
13.1%
Space Separator 27310
 
9.1%
Dash Punctuation 2059
 
0.7%
Open Punctuation 247
 
0.1%
Close Punctuation 247
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 42021
18.2%
e 27035
11.7%
o 26541
11.5%
i 18955
8.2%
r 16438
 
7.1%
a 16423
 
7.1%
t 16158
 
7.0%
m 14393
 
6.2%
d 12982
 
5.6%
v 12420
 
5.4%
Other values (12) 27567
11.9%
Uppercase Letter
ValueCountFrequency (%)
I 14646
37.1%
G 12461
31.6%
C 2037
 
5.2%
K 1966
 
5.0%
M 1951
 
4.9%
P 1829
 
4.6%
N 903
 
2.3%
F 670
 
1.7%
S 596
 
1.5%
L 442
 
1.1%
Other values (10) 1963
 
5.0%
Space Separator
ValueCountFrequency (%)
27310
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2059
100.0%
Open Punctuation
ValueCountFrequency (%)
( 247
100.0%
Close Punctuation
ValueCountFrequency (%)
) 247
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 270397
90.1%
Common 29863
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 42021
15.5%
e 27035
10.0%
o 26541
9.8%
i 18955
 
7.0%
r 16438
 
6.1%
a 16423
 
6.1%
t 16158
 
6.0%
I 14646
 
5.4%
m 14393
 
5.3%
d 12982
 
4.8%
Other values (32) 64805
24.0%
Common
ValueCountFrequency (%)
27310
91.5%
- 2059
 
6.9%
( 247
 
0.8%
) 247
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 300260
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 42021
14.0%
27310
 
9.1%
e 27035
 
9.0%
o 26541
 
8.8%
i 18955
 
6.3%
r 16438
 
5.5%
a 16423
 
5.5%
t 16158
 
5.4%
I 14646
 
4.9%
m 14393
 
4.8%
Other values (36) 80340
26.8%

side_b_dset_id
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3037.4637
Minimum141
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:57.074816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum141
5-th percentile193
Q1325
median325
Q39999
95-th percentile9999
Maximum9999
Range9858
Interquartile range (IQR)9674

Descriptive statistics

Standard deviation4306.1738
Coefficient of variation (CV)1.4176873
Kurtosis-1.025888
Mean3037.4637
Median Absolute Deviation (MAD)101
Skewness0.97240562
Sum53942318
Variance18543132
MonotonicityNot monotonic
2025-04-14T14:57:57.203285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
325 7173
40.4%
9999 4825
27.2%
195 2000
 
11.3%
142 637
 
3.6%
326 526
 
3.0%
193 269
 
1.5%
859 245
 
1.4%
319 214
 
1.2%
363 202
 
1.1%
224 169
 
1.0%
Other values (30) 1499
 
8.4%
ValueCountFrequency (%)
141 2
 
< 0.1%
142 637
 
3.6%
193 269
 
1.5%
194 62
 
0.3%
195 2000
11.3%
223 114
 
0.6%
224 169
 
1.0%
306 57
 
0.3%
307 114
 
0.6%
314 130
 
0.7%
ValueCountFrequency (%)
9999 4825
27.2%
6779 3
 
< 0.1%
6320 139
 
0.8%
5826 59
 
0.3%
4044 14
 
0.1%
3946 20
 
0.1%
3404 21
 
0.1%
2939 25
 
0.1%
2907 27
 
0.2%
2649 1
 
< 0.1%

side_b_new_id
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.07484
Minimum1
Maximum6779
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:57.321909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median319
Q3325
95-th percentile814
Maximum6779
Range6778
Interquartile range (IQR)324

Descriptive statistics

Standard deviation708.04273
Coefficient of variation (CV)2.2052265
Kurtosis53.230757
Mean321.07484
Median Absolute Deviation (MAD)95
Skewness7.023693
Sum5701968
Variance501324.51
MonotonicityNot monotonic
2025-04-14T14:57:57.434062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
325 7173
40.4%
1 4825
27.2%
195 2000
 
11.3%
142 637
 
3.6%
326 526
 
3.0%
193 269
 
1.5%
859 245
 
1.4%
319 214
 
1.2%
363 202
 
1.1%
224 169
 
1.0%
Other values (30) 1499
 
8.4%
ValueCountFrequency (%)
1 4825
27.2%
141 2
 
< 0.1%
142 637
 
3.6%
193 269
 
1.5%
194 62
 
0.3%
195 2000
11.3%
223 114
 
0.6%
224 169
 
1.0%
306 57
 
0.3%
307 114
 
0.6%
ValueCountFrequency (%)
6779 3
 
< 0.1%
6320 139
0.8%
5826 59
0.3%
4044 14
 
0.1%
3946 20
 
0.1%
3404 21
 
0.1%
2939 25
 
0.1%
2907 27
 
0.2%
2649 1
 
< 0.1%
2430 43
 
0.2%

side_b
Categorical

High correlation 

Distinct40
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size159.7 KiB
Kashmir insurgents
7173 
Civilians
4825 
CPI-Maoist
2000 
Government of Pakistan
 
637
ULFA
 
526
Other values (35)
2598 

Length

Max length26
Median length22
Mean length12.741596
Min length3

Characters and Unicode

Total characters226278
Distinct characters43
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowGovernment of Pakistan
2nd rowGovernment of Pakistan
3rd rowGovernment of Pakistan
4th rowGovernment of Pakistan
5th rowGovernment of Pakistan

Common Values

ValueCountFrequency (%)
Kashmir insurgents 7173
40.4%
Civilians 4825
27.2%
CPI-Maoist 2000
 
11.3%
Government of Pakistan 637
 
3.6%
ULFA 526
 
3.0%
PWG 269
 
1.5%
Muslims (India) 245
 
1.4%
Sikh insurgents 214
 
1.2%
NDFB 202
 
1.1%
NSCN-K 169
 
1.0%
Other values (30) 1499
 
8.4%

Length

2025-04-14T14:57:57.574872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
insurgents 7387
27.4%
kashmir 7173
26.6%
civilians 4825
17.9%
cpi-maoist 2000
 
7.4%
government 639
 
2.4%
of 639
 
2.4%
pakistan 637
 
2.4%
ulfa 526
 
2.0%
ndfb 305
 
1.1%
pwg 269
 
1.0%
Other values (36) 2540
 
9.4%

Most occurring characters

ValueCountFrequency (%)
i 32554
14.4%
s 29920
13.2%
n 21812
 
9.6%
a 15741
 
7.0%
r 15276
 
6.8%
t 10748
 
4.7%
9181
 
4.1%
e 8796
 
3.9%
m 8122
 
3.6%
u 7674
 
3.4%
Other values (33) 66454
29.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 181262
80.1%
Uppercase Letter 32733
 
14.5%
Space Separator 9181
 
4.1%
Dash Punctuation 2612
 
1.2%
Close Punctuation 245
 
0.1%
Open Punctuation 245
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
K 7585
23.2%
C 7411
22.6%
P 3341
10.2%
M 2468
 
7.5%
I 2381
 
7.3%
N 1593
 
4.9%
F 1419
 
4.3%
L 1223
 
3.7%
G 984
 
3.0%
U 882
 
2.7%
Other values (10) 3446
10.5%
Lowercase Letter
ValueCountFrequency (%)
i 32554
18.0%
s 29920
16.5%
n 21812
12.0%
a 15741
8.7%
r 15276
8.4%
t 10748
 
5.9%
e 8796
 
4.9%
m 8122
 
4.5%
u 7674
 
4.2%
g 7438
 
4.1%
Other values (9) 23181
12.8%
Space Separator
ValueCountFrequency (%)
9181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2612
100.0%
Close Punctuation
ValueCountFrequency (%)
) 245
100.0%
Open Punctuation
ValueCountFrequency (%)
( 245
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 213995
94.6%
Common 12283
 
5.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 32554
15.2%
s 29920
14.0%
n 21812
 
10.2%
a 15741
 
7.4%
r 15276
 
7.1%
t 10748
 
5.0%
e 8796
 
4.1%
m 8122
 
3.8%
u 7674
 
3.6%
K 7585
 
3.5%
Other values (29) 55767
26.1%
Common
ValueCountFrequency (%)
9181
74.7%
- 2612
 
21.3%
) 245
 
2.0%
( 245
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 226278
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 32554
14.4%
s 29920
13.2%
n 21812
 
9.6%
a 15741
 
7.0%
r 15276
 
6.8%
t 10748
 
4.7%
9181
 
4.1%
e 8796
 
3.9%
m 8122
 
3.6%
u 7674
 
3.4%
Other values (33) 66454
29.4%

number_of_sources
Real number (ℝ)

Distinct18
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12421871
Minimum-1
Maximum25
Zeros0
Zeros (%)0.0%
Negative9639
Negative (%)54.3%
Memory size277.5 KiB
2025-04-14T14:57:57.682850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q31
95-th percentile2
Maximum25
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4440805
Coefficient of variation (CV)11.625306
Kurtosis15.314054
Mean0.12421871
Median Absolute Deviation (MAD)0
Skewness2.1690167
Sum2206
Variance2.0853685
MonotonicityNot monotonic
2025-04-14T14:57:57.791142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
-1 9639
54.3%
1 6006
33.8%
2 1345
 
7.6%
3 441
 
2.5%
4 154
 
0.9%
5 64
 
0.4%
6 44
 
0.2%
7 23
 
0.1%
10 9
 
0.1%
9 9
 
0.1%
Other values (8) 25
 
0.1%
ValueCountFrequency (%)
-1 9639
54.3%
1 6006
33.8%
2 1345
 
7.6%
3 441
 
2.5%
4 154
 
0.9%
5 64
 
0.4%
6 44
 
0.2%
7 23
 
0.1%
8 8
 
< 0.1%
9 9
 
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
23 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 8
< 0.1%
10 9
0.1%
9 9
0.1%
8 8
< 0.1%
Distinct11027
Distinct (%)62.1%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
2025-04-14T14:57:58.065043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length2665
Median length707
Mean length90.444901
Min length11

Characters and Unicode

Total characters1606211
Distinct characters98
Distinct categories16 ?
Distinct scripts2 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8686 ?
Unique (%)48.9%

Sample

1st rowAGGREGATION
2nd rowAGGREGATION
3rd rowReuters 13/4 1990
4th rowReuters 21/4 1990, 22/4 1990
5th rowReuters 6/5 1990
ValueCountFrequency (%)
in 13085
 
6.3%
of 7510
 
3.6%
killed 7506
 
3.6%
trust 5866
 
2.8%
press 4850
 
2.3%
kashmir 4724
 
2.3%
satp 4067
 
2.0%
india 3292
 
1.6%
timeline 3117
 
1.5%
militants 2772
 
1.3%
Other values (17781) 149972
72.5%
2025-04-14T14:57:58.473477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
192335
 
12.0%
i 99101
 
6.2%
e 94928
 
5.9%
s 79054
 
4.9%
a 75075
 
4.7%
n 70964
 
4.4%
r 69030
 
4.3%
t 63567
 
4.0%
0 53987
 
3.4%
o 49643
 
3.1%
Other values (88) 758527
47.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 883826
55.0%
Uppercase Letter 201003
 
12.5%
Space Separator 192335
 
12.0%
Decimal Number 179425
 
11.2%
Other Punctuation 97206
 
6.1%
Dash Punctuation 37340
 
2.3%
Close Punctuation 6757
 
0.4%
Open Punctuation 6744
 
0.4%
Control 1159
 
0.1%
Final Punctuation 173
 
< 0.1%
Other values (6) 243
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 99101
11.2%
e 94928
10.7%
s 79054
 
8.9%
a 75075
 
8.5%
n 70964
 
8.0%
r 69030
 
7.8%
t 63567
 
7.2%
o 49643
 
5.6%
l 48386
 
5.5%
d 36038
 
4.1%
Other values (17) 198040
22.4%
Uppercase Letter
ValueCountFrequency (%)
T 28731
14.3%
P 22816
11.4%
A 20680
10.3%
I 19684
 
9.8%
S 17201
 
8.6%
K 10003
 
5.0%
N 8815
 
4.4%
M 8452
 
4.2%
B 7591
 
3.8%
C 7417
 
3.7%
Other values (16) 49613
24.7%
Other Punctuation
ValueCountFrequency (%)
, 33404
34.4%
" 29559
30.4%
. 12120
 
12.5%
/ 8934
 
9.2%
: 6031
 
6.2%
; 4186
 
4.3%
' 1625
 
1.7%
& 1311
 
1.3%
? 14
 
< 0.1%
% 12
 
< 0.1%
Other values (3) 10
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 53987
30.1%
2 38850
21.7%
1 33947
18.9%
9 12960
 
7.2%
3 9160
 
5.1%
4 7115
 
4.0%
8 6400
 
3.6%
5 5983
 
3.3%
6 5565
 
3.1%
7 5458
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 37334
> 99.9%
5
 
< 0.1%
1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
= 5
45.5%
| 5
45.5%
+ 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 6713
99.3%
] 44
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 6698
99.3%
[ 46
 
0.7%
Control
ValueCountFrequency (%)
1064
91.8%
95
 
8.2%
Final Punctuation
ValueCountFrequency (%)
163
94.2%
10
 
5.8%
Initial Punctuation
ValueCountFrequency (%)
57
83.8%
11
 
16.2%
Modifier Symbol
ValueCountFrequency (%)
` 12
85.7%
´ 2
 
14.3%
Space Separator
ValueCountFrequency (%)
192335
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 148
100.0%
Currency Symbol
ValueCountFrequency (%)
1
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1084829
67.5%
Common 521382
32.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 99101
 
9.1%
e 94928
 
8.8%
s 79054
 
7.3%
a 75075
 
6.9%
n 70964
 
6.5%
r 69030
 
6.4%
t 63567
 
5.9%
o 49643
 
4.6%
l 48386
 
4.5%
d 36038
 
3.3%
Other values (43) 399043
36.8%
Common
ValueCountFrequency (%)
192335
36.9%
0 53987
 
10.4%
2 38850
 
7.5%
- 37334
 
7.2%
1 33947
 
6.5%
, 33404
 
6.4%
" 29559
 
5.7%
9 12960
 
2.5%
. 12120
 
2.3%
3 9160
 
1.8%
Other values (35) 67726
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1605953
> 99.9%
Punctuation 253
 
< 0.1%
None 3
 
< 0.1%
Currency Symbols 1
 
< 0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
192335
 
12.0%
i 99101
 
6.2%
e 94928
 
5.9%
s 79054
 
4.9%
a 75075
 
4.7%
n 70964
 
4.4%
r 69030
 
4.3%
t 63567
 
4.0%
0 53987
 
3.4%
o 49643
 
3.1%
Other values (77) 758269
47.2%
Punctuation
ValueCountFrequency (%)
163
64.4%
57
 
22.5%
11
 
4.3%
10
 
4.0%
6
 
2.4%
5
 
2.0%
1
 
0.4%
None
ValueCountFrequency (%)
´ 2
66.7%
â 1
33.3%
Currency Symbols
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%
Distinct1860
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
2025-04-14T14:57:58.736779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length1350
Median length183
Mean length11.126358
Min length2

Characters and Unicode

Total characters197593
Distinct characters82
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1493 ?
Unique (%)8.4%

Sample

1st rowAGGREGATION EVENT
2nd rowAGGREGATION EVENT
3rd rowUnited News of India
4th rowPress Trust of India
5th rowIndian officials
ValueCountFrequency (%)
police 6459
21.8%
satp 3957
13.4%
unknown 2807
 
9.5%
official 1850
 
6.3%
spokesman 1355
 
4.6%
officials 1351
 
4.6%
sources 1071
 
3.6%
of 663
 
2.2%
army 609
 
2.1%
defence 347
 
1.2%
Other values (1632) 9125
30.8%
2025-04-14T14:57:59.167485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 17447
 
8.8%
i 17347
 
8.8%
e 15692
 
7.9%
n 14850
 
7.5%
c 12359
 
6.3%
11833
 
6.0%
l 11323
 
5.7%
a 10254
 
5.2%
s 9841
 
5.0%
f 8070
 
4.1%
Other values (72) 68577
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 154343
78.1%
Uppercase Letter 28288
 
14.3%
Space Separator 11833
 
6.0%
Other Punctuation 1779
 
0.9%
Open Punctuation 442
 
0.2%
Close Punctuation 441
 
0.2%
Control 201
 
0.1%
Dash Punctuation 195
 
0.1%
Decimal Number 66
 
< 0.1%
Final Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 17447
11.3%
i 17347
11.2%
e 15692
10.2%
n 14850
9.6%
c 12359
8.0%
l 11323
 
7.3%
a 10254
 
6.6%
s 9841
 
6.4%
f 8070
 
5.2%
p 7941
 
5.1%
Other values (16) 29219
18.9%
Uppercase Letter
ValueCountFrequency (%)
P 5816
20.6%
S 5029
17.8%
A 4430
15.7%
T 4260
15.1%
U 2890
10.2%
I 904
 
3.2%
D 634
 
2.2%
O 615
 
2.2%
G 491
 
1.7%
K 415
 
1.5%
Other values (16) 2804
9.9%
Other Punctuation
ValueCountFrequency (%)
, 1135
63.8%
. 273
 
15.3%
; 127
 
7.1%
/ 115
 
6.5%
' 63
 
3.5%
" 41
 
2.3%
: 9
 
0.5%
& 6
 
0.3%
! 6
 
0.3%
? 4
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 18
27.3%
2 14
21.2%
0 12
18.2%
8 6
 
9.1%
4 5
 
7.6%
3 4
 
6.1%
9 3
 
4.5%
6 2
 
3.0%
7 1
 
1.5%
5 1
 
1.5%
Close Punctuation
ValueCountFrequency (%)
) 433
98.2%
] 8
 
1.8%
Open Punctuation
ValueCountFrequency (%)
( 433
98.0%
[ 9
 
2.0%
Control
ValueCountFrequency (%)
200
99.5%
1
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 193
99.0%
2
 
1.0%
Space Separator
ValueCountFrequency (%)
11833
100.0%
Final Punctuation
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 182631
92.4%
Common 14962
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 17447
 
9.6%
i 17347
 
9.5%
e 15692
 
8.6%
n 14850
 
8.1%
c 12359
 
6.8%
l 11323
 
6.2%
a 10254
 
5.6%
s 9841
 
5.4%
f 8070
 
4.4%
p 7941
 
4.3%
Other values (42) 57507
31.5%
Common
ValueCountFrequency (%)
11833
79.1%
, 1135
 
7.6%
) 433
 
2.9%
( 433
 
2.9%
. 273
 
1.8%
200
 
1.3%
- 193
 
1.3%
; 127
 
0.8%
/ 115
 
0.8%
' 63
 
0.4%
Other values (20) 157
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 197586
> 99.9%
Punctuation 7
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 17447
 
8.8%
i 17347
 
8.8%
e 15692
 
7.9%
n 14850
 
7.5%
c 12359
 
6.3%
11833
 
6.0%
l 11323
 
5.7%
a 10254
 
5.2%
s 9841
 
5.0%
f 8070
 
4.1%
Other values (70) 68570
34.7%
Punctuation
ValueCountFrequency (%)
5
71.4%
2
 
28.6%

where_prec
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2008559
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:57:59.251236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.0582895
Coefficient of variation (CV)0.48085362
Kurtosis-0.23049334
Mean2.2008559
Median Absolute Deviation (MAD)1
Skewness0.56596025
Sum39085
Variance1.1199767
MonotonicityNot monotonic
2025-04-14T14:57:59.319186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 5688
32.0%
3 5241
29.5%
2 5090
28.7%
4 1228
 
6.9%
5 491
 
2.8%
6 20
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1 5688
32.0%
2 5090
28.7%
3 5241
29.5%
4 1228
 
6.9%
5 491
 
2.8%
6 20
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 20
 
0.1%
5 491
 
2.8%
4 1228
 
6.9%
3 5241
29.5%
2 5090
28.7%
1 5688
32.0%
Distinct4799
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-04-14T14:57:59.553693image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length61
Median length51
Mean length16.861141
Min length3

Characters and Unicode

Total characters299437
Distinct characters71
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3277 ?
Unique (%)18.5%

Sample

1st rowLoC
2nd rowLoC
3rd rowLoC
4th rowPoonch district
5th rowLoC
ValueCountFrequency (%)
village 6087
 
14.7%
district 5357
 
13.0%
town 3874
 
9.4%
state 1229
 
3.0%
sub-district 943
 
2.3%
kashmir 822
 
2.0%
srinagar 745
 
1.8%
jammu 658
 
1.6%
and 603
 
1.5%
kupwara 451
 
1.1%
Other values (4823) 20527
49.7%
2025-04-14T14:57:59.933664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 38395
 
12.8%
i 28102
 
9.4%
23543
 
7.9%
t 23198
 
7.7%
r 20462
 
6.8%
l 18407
 
6.1%
n 13374
 
4.5%
e 12221
 
4.1%
s 12139
 
4.1%
d 11036
 
3.7%
Other values (61) 98560
32.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 251168
83.9%
Space Separator 23543
 
7.9%
Uppercase Letter 22379
 
7.5%
Dash Punctuation 1271
 
0.4%
Other Punctuation 880
 
0.3%
Decimal Number 115
 
< 0.1%
Close Punctuation 39
 
< 0.1%
Open Punctuation 39
 
< 0.1%
Control 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 38395
15.3%
i 28102
11.2%
t 23198
 
9.2%
r 20462
 
8.1%
l 18407
 
7.3%
n 13374
 
5.3%
e 12221
 
4.9%
s 12139
 
4.8%
d 11036
 
4.4%
o 10979
 
4.4%
Other values (18) 62855
25.0%
Uppercase Letter
ValueCountFrequency (%)
K 3422
15.3%
B 2413
10.8%
S 2359
10.5%
P 1866
 
8.3%
D 1326
 
5.9%
A 1168
 
5.2%
M 1165
 
5.2%
G 1153
 
5.2%
T 1093
 
4.9%
C 1031
 
4.6%
Other values (16) 5383
24.1%
Decimal Number
ValueCountFrequency (%)
7 26
22.6%
4 22
19.1%
5 19
16.5%
8 17
14.8%
0 13
11.3%
3 6
 
5.2%
9 5
 
4.3%
2 4
 
3.5%
1 3
 
2.6%
Other Punctuation
ValueCountFrequency (%)
, 831
94.4%
. 29
 
3.3%
/ 20
 
2.3%
Space Separator
ValueCountFrequency (%)
23543
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1271
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Open Punctuation
ValueCountFrequency (%)
( 39
100.0%
Control
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 273547
91.4%
Common 25890
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 38395
14.0%
i 28102
 
10.3%
t 23198
 
8.5%
r 20462
 
7.5%
l 18407
 
6.7%
n 13374
 
4.9%
e 12221
 
4.5%
s 12139
 
4.4%
d 11036
 
4.0%
o 10979
 
4.0%
Other values (44) 85234
31.2%
Common
ValueCountFrequency (%)
23543
90.9%
- 1271
 
4.9%
, 831
 
3.2%
) 39
 
0.2%
( 39
 
0.2%
. 29
 
0.1%
7 26
 
0.1%
4 22
 
0.1%
/ 20
 
0.1%
5 19
 
0.1%
Other values (7) 51
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 299423
> 99.9%
None 14
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 38395
 
12.8%
i 28102
 
9.4%
23543
 
7.9%
t 23198
 
7.7%
r 20462
 
6.8%
l 18407
 
6.1%
n 13374
 
4.5%
e 12221
 
4.1%
s 12139
 
4.1%
d 11036
 
3.7%
Other values (59) 98546
32.9%
None
ValueCountFrequency (%)
ā 12
85.7%
ī 2
 
14.3%
Distinct14281
Distinct (%)80.4%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
2025-04-14T14:58:00.209021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14898
Median length346
Mean length45.269216
Min length3

Characters and Unicode

Total characters803936
Distinct characters92
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13612 ?
Unique (%)76.6%

Sample

1st rowIndia - Pakistan border
2nd rowIndia - Pakistan border
3rd rowKashmir no-man's-land
4th rowBaglairdara
5th rowKashmir no-man's-land
ValueCountFrequency (%)
in 9559
 
8.0%
district 9266
 
7.7%
of 7119
 
5.9%
village 4179
 
3.5%
the 3800
 
3.2%
area 3547
 
3.0%
near 2173
 
1.8%
police 1889
 
1.6%
station 1720
 
1.4%
under 1688
 
1.4%
Other values (14285) 74913
62.5%
2025-04-14T14:58:00.616789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102508
12.8%
a 92338
 
11.5%
i 66250
 
8.2%
r 56243
 
7.0%
t 50790
 
6.3%
n 43771
 
5.4%
e 40277
 
5.0%
o 38052
 
4.7%
s 30499
 
3.8%
l 29062
 
3.6%
Other values (82) 254146
31.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 616310
76.7%
Space Separator 102552
 
12.8%
Uppercase Letter 60898
 
7.6%
Other Punctuation 9538
 
1.2%
Decimal Number 5470
 
0.7%
Close Punctuation 2648
 
0.3%
Open Punctuation 2645
 
0.3%
Control 1972
 
0.2%
Dash Punctuation 1823
 
0.2%
Final Punctuation 62
 
< 0.1%
Other values (4) 18
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 92338
15.0%
i 66250
10.7%
r 56243
 
9.1%
t 50790
 
8.2%
n 43771
 
7.1%
e 40277
 
6.5%
o 38052
 
6.2%
s 30499
 
4.9%
l 29062
 
4.7%
h 26391
 
4.3%
Other values (17) 142637
23.1%
Uppercase Letter
ValueCountFrequency (%)
D 7172
11.8%
K 7125
11.7%
S 5901
 
9.7%
B 5402
 
8.9%
P 5038
 
8.3%
A 3594
 
5.9%
C 3174
 
5.2%
M 3106
 
5.1%
N 2458
 
4.0%
G 2434
 
4.0%
Other values (16) 15494
25.4%
Other Punctuation
ValueCountFrequency (%)
, 5587
58.6%
. 2311
24.2%
' 773
 
8.1%
/ 215
 
2.3%
" 212
 
2.2%
: 198
 
2.1%
& 110
 
1.2%
? 66
 
0.7%
; 60
 
0.6%
5
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 1416
25.9%
5 845
15.4%
1 644
11.8%
2 625
11.4%
3 477
 
8.7%
4 465
 
8.5%
7 316
 
5.8%
8 255
 
4.7%
6 255
 
4.7%
9 172
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 1821
99.9%
1
 
0.1%
1
 
0.1%
Math Symbol
ValueCountFrequency (%)
= 10
83.3%
< 1
 
8.3%
~ 1
 
8.3%
Space Separator
ValueCountFrequency (%)
102508
> 99.9%
  44
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 2549
96.3%
] 99
 
3.7%
Open Punctuation
ValueCountFrequency (%)
( 2545
96.2%
[ 100
 
3.8%
Final Punctuation
ValueCountFrequency (%)
61
98.4%
1
 
1.6%
Control
ValueCountFrequency (%)
1972
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Modifier Symbol
ValueCountFrequency (%)
¨ 2
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 677208
84.2%
Common 126728
 
15.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 92338
13.6%
i 66250
 
9.8%
r 56243
 
8.3%
t 50790
 
7.5%
n 43771
 
6.5%
e 40277
 
5.9%
o 38052
 
5.6%
s 30499
 
4.5%
l 29062
 
4.3%
h 26391
 
3.9%
Other values (43) 203535
30.1%
Common
ValueCountFrequency (%)
102508
80.9%
, 5587
 
4.4%
) 2549
 
2.0%
( 2545
 
2.0%
. 2311
 
1.8%
1972
 
1.6%
- 1821
 
1.4%
0 1416
 
1.1%
5 845
 
0.7%
' 773
 
0.6%
Other values (29) 4401
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 803819
> 99.9%
Punctuation 70
 
< 0.1%
None 47
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
102508
12.8%
a 92338
 
11.5%
i 66250
 
8.2%
r 56243
 
7.0%
t 50790
 
6.3%
n 43771
 
5.4%
e 40277
 
5.0%
o 38052
 
4.7%
s 30499
 
3.8%
l 29062
 
3.6%
Other values (73) 254029
31.6%
Punctuation
ValueCountFrequency (%)
61
87.1%
5
 
7.1%
1
 
1.4%
1
 
1.4%
1
 
1.4%
1
 
1.4%
None
ValueCountFrequency (%)
  44
93.6%
¨ 2
 
4.3%
å 1
 
2.1%

adm_1
Categorical

High correlation 

Distinct33
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
Jammu and Kashmir state
9616 
Assam state
1581 
Chhattisgarh state
1443 
Jharkhand state
 
679
Manipur state
 
634
Other values (28)
3806 

Length

Max length29
Median length23
Mean length19.128498
Min length7

Characters and Unicode

Total characters339703
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowJammu and Kashmir state
5th rowUnknown

Common Values

ValueCountFrequency (%)
Jammu and Kashmir state 9616
54.1%
Assam state 1581
 
8.9%
Chhattisgarh state 1443
 
8.1%
Jharkhand state 679
 
3.8%
Manipur state 634
 
3.6%
Andhra Pradesh state 547
 
3.1%
Tripura state 413
 
2.3%
Bihar state 390
 
2.2%
Punjab state 389
 
2.2%
Maharashtra state 323
 
1.8%
Other values (23) 1744
 
9.8%

Length

2025-04-14T14:58:00.727214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
state 17563
31.5%
jammu 9616
17.2%
and 9616
17.2%
kashmir 9616
17.2%
assam 1581
 
2.8%
chhattisgarh 1443
 
2.6%
pradesh 745
 
1.3%
jharkhand 679
 
1.2%
manipur 634
 
1.1%
andhra 547
 
1.0%
Other values (32) 3767
 
6.8%

Most occurring characters

ValueCountFrequency (%)
a 59620
17.6%
t 39274
11.6%
38048
11.2%
s 33852
10.0%
m 30444
9.0%
e 19180
 
5.6%
h 18146
 
5.3%
r 16296
 
4.8%
i 13229
 
3.9%
n 13077
 
3.8%
Other values (30) 58537
17.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 273124
80.4%
Space Separator 38048
 
11.2%
Uppercase Letter 28531
 
8.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 59620
21.8%
t 39274
14.4%
s 33852
12.4%
m 30444
11.1%
e 19180
 
7.0%
h 18146
 
6.6%
r 16296
 
6.0%
i 13229
 
4.8%
n 13077
 
4.8%
d 12183
 
4.5%
Other values (13) 17823
 
6.5%
Uppercase Letter
ValueCountFrequency (%)
J 10295
36.1%
K 9662
33.9%
A 2176
 
7.6%
C 1516
 
5.3%
P 1134
 
4.0%
M 1105
 
3.9%
B 711
 
2.5%
O 463
 
1.6%
T 453
 
1.6%
N 336
 
1.2%
Other values (6) 680
 
2.4%
Space Separator
ValueCountFrequency (%)
38048
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 301655
88.8%
Common 38048
 
11.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 59620
19.8%
t 39274
13.0%
s 33852
11.2%
m 30444
10.1%
e 19180
 
6.4%
h 18146
 
6.0%
r 16296
 
5.4%
i 13229
 
4.4%
n 13077
 
4.3%
d 12183
 
4.0%
Other values (29) 46354
15.4%
Common
ValueCountFrequency (%)
38048
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 339703
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 59620
17.6%
t 39274
11.6%
38048
11.2%
s 33852
10.0%
m 30444
9.0%
e 19180
 
5.6%
h 18146
 
5.3%
r 16296
 
4.8%
i 13229
 
3.9%
n 13077
 
3.8%
Other values (30) 58537
17.2%

adm_2
Text

Distinct353
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.4 MiB
2025-04-14T14:58:00.984779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length37
Median length30
Mean length15.920435
Min length7

Characters and Unicode

Total characters282731
Distinct characters54
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92 ?
Unique (%)0.5%

Sample

1st rowUnknown
2nd rowUnknown
3rd rowUnknown
4th rowPoonch district
5th rowUnknown
ValueCountFrequency (%)
district 16098
45.7%
unknown 1660
 
4.7%
baramulla 1275
 
3.6%
kupwara 1241
 
3.5%
srinagar 867
 
2.5%
poonch 835
 
2.4%
anantnag 828
 
2.4%
pulwama 757
 
2.1%
doda 670
 
1.9%
rajouri 639
 
1.8%
Other values (355) 10346
29.4%
2025-04-14T14:58:01.374308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 38748
13.7%
t 35486
12.6%
r 27383
9.7%
a 26165
9.3%
d 19446
 
6.9%
s 17920
 
6.3%
17457
 
6.2%
c 17391
 
6.2%
n 12965
 
4.6%
u 7871
 
2.8%
Other values (44) 61899
21.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 246091
87.0%
Uppercase Letter 19141
 
6.8%
Space Separator 17457
 
6.2%
Dash Punctuation 21
 
< 0.1%
Open Punctuation 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%
Decimal Number 2
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 38748
15.7%
t 35486
14.4%
r 27383
11.1%
a 26165
10.6%
d 19446
7.9%
s 17920
7.3%
c 17391
7.1%
n 12965
 
5.3%
u 7871
 
3.2%
o 7296
 
3.0%
Other values (15) 35420
14.4%
Uppercase Letter
ValueCountFrequency (%)
K 2741
14.3%
B 2539
13.3%
U 2024
10.6%
S 1842
9.6%
P 1756
9.2%
D 1414
7.4%
A 1140
 
6.0%
R 877
 
4.6%
G 791
 
4.1%
W 659
 
3.4%
Other values (12) 3358
17.5%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
17457
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 265232
93.8%
Common 17499
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 38748
14.6%
t 35486
13.4%
r 27383
10.3%
a 26165
9.9%
d 19446
 
7.3%
s 17920
 
6.8%
c 17391
 
6.6%
n 12965
 
4.9%
u 7871
 
3.0%
o 7296
 
2.8%
Other values (37) 54561
20.6%
Common
ValueCountFrequency (%)
17457
99.8%
- 21
 
0.1%
( 9
 
0.1%
) 9
 
0.1%
/ 1
 
< 0.1%
2 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 282731
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 38748
13.7%
t 35486
12.6%
r 27383
9.7%
a 26165
9.3%
d 19446
 
6.9%
s 17920
 
6.3%
17457
 
6.2%
c 17391
 
6.2%
n 12965
 
4.6%
u 7871
 
2.8%
Other values (44) 61899
21.9%

latitude
Real number (ℝ)

High correlation 

Distinct4808
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.004885
Minimum10.216061
Maximum35.174722
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:01.544457image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.216061
5-th percentile18.404942
Q124.313228
median33.10081
Q333.91667
95-th percentile34.392806
Maximum35.174722
Range24.958661
Interquartile range (IQR)9.603442

Descriptive statistics

Standard deviation5.8982175
Coefficient of variation (CV)0.20335256
Kurtosis-0.98390378
Mean29.004885
Median Absolute Deviation (MAD)1.297008
Skewness-0.69515721
Sum515097.75
Variance34.78897
MonotonicityNot monotonic
2025-04-14T14:58:01.712600image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.080399 599
 
3.4%
33.91667 578
 
3.3%
34.372601 398
 
2.2%
34.19287 299
 
1.7%
33.10081 278
 
1.6%
33.83333 263
 
1.5%
34.16667 229
 
1.3%
33.77 225
 
1.3%
34.274824 213
 
1.2%
27.5 213
 
1.2%
Other values (4798) 14464
81.4%
ValueCountFrequency (%)
10.216061 1
 
< 0.1%
10.41667 2
 
< 0.1%
10.979356 1
 
< 0.1%
11 2
 
< 0.1%
11.07277 1
 
< 0.1%
11.27057 1
 
< 0.1%
11.280059 1
 
< 0.1%
11.551837 1
 
< 0.1%
11.719363 2
 
< 0.1%
12.5024 5
< 0.1%
ValueCountFrequency (%)
35.174722 1
 
< 0.1%
35.10685 7
< 0.1%
35.02235 1
 
< 0.1%
34.75428 1
 
< 0.1%
34.74185 1
 
< 0.1%
34.735826 2
 
< 0.1%
34.72272 1
 
< 0.1%
34.72 11
0.1%
34.71 13
0.1%
34.704549 2
 
< 0.1%

longitude
Real number (ℝ)

High correlation 

Distinct4812
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.835733
Minimum65.997296
Maximum96.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:01.883422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum65.997296
5-th percentile74.1
Q174.676
median75.517472
Q384.126548
95-th percentile93.966667
Maximum96.5
Range30.502704
Interquartile range (IQR)9.450548

Descriptive statistics

Standard deviation6.9067055
Coefficient of variation (CV)0.086511456
Kurtosis-0.4155245
Mean79.835733
Median Absolute Deviation (MAD)1.350179
Skewness1.0234703
Sum1417802.8
Variance47.702581
MonotonicityNot monotonic
2025-04-14T14:58:02.032262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.823383 599
 
3.4%
76.66667 578
 
3.3%
74.167293 398
 
2.2%
75.16667 381
 
2.1%
74.3692 299
 
1.7%
75.6497 278
 
1.6%
74.75 229
 
1.3%
74.1 225
 
1.3%
74.475391 213
 
1.2%
74.25 210
 
1.2%
Other values (4802) 14349
80.8%
ValueCountFrequency (%)
65.997296 1
 
< 0.1%
71.75 12
 
0.1%
72.294349 1
 
< 0.1%
72.296657 2
 
< 0.1%
72.334114 1
 
< 0.1%
72.440944 1
 
< 0.1%
72.561004 1
 
< 0.1%
72.592201 32
0.2%
72.65702 6
 
< 0.1%
72.680454 1
 
< 0.1%
ValueCountFrequency (%)
96.5 1
 
< 0.1%
96.34518 4
< 0.1%
96.18118 1
 
< 0.1%
96.1 2
< 0.1%
96.060668 1
 
< 0.1%
96.023765 1
 
< 0.1%
95.97406 2
< 0.1%
95.883732 1
 
< 0.1%
95.875785 1
 
< 0.1%
95.87135 1
 
< 0.1%
Distinct4839
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2025-04-14T14:58:02.342124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length45
Median length27
Mean length25.472211
Min length13

Characters and Unicode

Total characters452361
Distinct characters19
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3304 ?
Unique (%)18.6%

Sample

1st rowPOINT (74.676 32.565)
2nd rowPOINT (74.676 32.565)
3rd rowPOINT (74.676 32.565)
4th rowPOINT (74.1 33.77)
5th rowPOINT (74.676 32.565)
ValueCountFrequency (%)
point 17759
33.3%
74.823383 599
 
1.1%
34.080399 599
 
1.1%
33.91667 578
 
1.1%
76.66667 578
 
1.1%
74.167293 398
 
0.7%
34.372601 398
 
0.7%
75.16667 381
 
0.7%
74.3692 299
 
0.6%
34.19287 299
 
0.6%
Other values (9621) 31389
58.9%
2025-04-14T14:58:02.812216image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 40173
 
8.9%
35518
 
7.9%
. 34460
 
7.6%
7 32244
 
7.1%
6 28661
 
6.3%
4 27987
 
6.2%
2 24608
 
5.4%
8 23655
 
5.2%
9 23359
 
5.2%
1 22109
 
4.9%
Other values (9) 159587
35.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258070
57.0%
Uppercase Letter 88795
 
19.6%
Space Separator 35518
 
7.9%
Other Punctuation 34460
 
7.6%
Open Punctuation 17759
 
3.9%
Close Punctuation 17759
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 40173
15.6%
7 32244
12.5%
6 28661
11.1%
4 27987
10.8%
2 24608
9.5%
8 23655
9.2%
9 23359
9.1%
1 22109
8.6%
5 19562
7.6%
0 15712
 
6.1%
Uppercase Letter
ValueCountFrequency (%)
I 17759
20.0%
O 17759
20.0%
P 17759
20.0%
T 17759
20.0%
N 17759
20.0%
Space Separator
ValueCountFrequency (%)
35518
100.0%
Other Punctuation
ValueCountFrequency (%)
. 34460
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17759
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 363566
80.4%
Latin 88795
 
19.6%

Most frequent character per script

Common
ValueCountFrequency (%)
3 40173
11.0%
35518
9.8%
. 34460
9.5%
7 32244
8.9%
6 28661
7.9%
4 27987
 
7.7%
2 24608
 
6.8%
8 23655
 
6.5%
9 23359
 
6.4%
1 22109
 
6.1%
Other values (4) 70792
19.5%
Latin
ValueCountFrequency (%)
I 17759
20.0%
O 17759
20.0%
P 17759
20.0%
T 17759
20.0%
N 17759
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 452361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 40173
 
8.9%
35518
 
7.9%
. 34460
 
7.6%
7 32244
 
7.1%
6 28661
 
6.3%
4 27987
 
6.2%
2 24608
 
5.4%
8 23655
 
5.2%
9 23359
 
5.2%
1 22109
 
4.9%
Other values (9) 159587
35.3%

priogrid_gid
Real number (ℝ)

High correlation 

Distinct481
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean171534.53
Minimum144514
Maximum180517
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:02.953070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum144514
5-th percentile156044
Q1164705
median177629
Q3178354
95-th percentile179070
Maximum180517
Range36003
Interquartile range (IQR)13649

Descriptive statistics

Standard deviation8486.9588
Coefficient of variation (CV)0.049476679
Kurtosis-0.970029
Mean171534.53
Median Absolute Deviation (MAD)1441
Skewness-0.70195899
Sum3.0462817 × 109
Variance72028470
MonotonicityNot monotonic
2025-04-14T14:58:03.082234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179069 1787
 
10.1%
179070 1581
 
8.9%
178351 1084
 
6.1%
178350 1028
 
5.8%
178349 879
 
4.9%
178354 578
 
3.3%
177629 518
 
2.9%
177632 513
 
2.9%
177631 368
 
2.1%
156762 300
 
1.7%
Other values (471) 9123
51.4%
ValueCountFrequency (%)
144514 2
< 0.1%
144515 1
< 0.1%
145234 1
< 0.1%
145952 1
< 0.1%
145953 1
< 0.1%
145954 1
< 0.1%
145957 2
< 0.1%
146672 2
< 0.1%
146673 1
< 0.1%
148110 1
< 0.1%
ValueCountFrequency (%)
180517 1
 
< 0.1%
180515 8
 
< 0.1%
179794 18
 
0.1%
179793 32
 
0.2%
179792 4
 
< 0.1%
179791 3
 
< 0.1%
179790 80
0.5%
179789 123
0.7%
179788 58
0.3%
179077 1
 
< 0.1%

country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
India
17759 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters88795
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndia
2nd rowIndia
3rd rowIndia
4th rowIndia
5th rowIndia

Common Values

ValueCountFrequency (%)
India 17759
100.0%

Length

2025-04-14T14:58:03.199466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:58:03.252141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
india 17759
100.0%

Most occurring characters

ValueCountFrequency (%)
I 17759
20.0%
n 17759
20.0%
d 17759
20.0%
i 17759
20.0%
a 17759
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 71036
80.0%
Uppercase Letter 17759
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 17759
25.0%
d 17759
25.0%
i 17759
25.0%
a 17759
25.0%
Uppercase Letter
ValueCountFrequency (%)
I 17759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88795
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 17759
20.0%
n 17759
20.0%
d 17759
20.0%
i 17759
20.0%
a 17759
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88795
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 17759
20.0%
n 17759
20.0%
d 17759
20.0%
i 17759
20.0%
a 17759
20.0%

iso3
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
IND
17759 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters53277
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIND
2nd rowIND
3rd rowIND
4th rowIND
5th rowIND

Common Values

ValueCountFrequency (%)
IND 17759
100.0%

Length

2025-04-14T14:58:03.310354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:58:03.369552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ind 17759
100.0%

Most occurring characters

ValueCountFrequency (%)
I 17759
33.3%
N 17759
33.3%
D 17759
33.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 53277
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 17759
33.3%
N 17759
33.3%
D 17759
33.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 53277
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 17759
33.3%
N 17759
33.3%
D 17759
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 17759
33.3%
N 17759
33.3%
D 17759
33.3%

country_id
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
750
17759 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters53277
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row750
2nd row750
3rd row750
4th row750
5th row750

Common Values

ValueCountFrequency (%)
750 17759
100.0%

Length

2025-04-14T14:58:03.431220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:58:03.488697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
750 17759
100.0%

Most occurring characters

ValueCountFrequency (%)
7 17759
33.3%
5 17759
33.3%
0 17759
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53277
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 17759
33.3%
5 17759
33.3%
0 17759
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 53277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
7 17759
33.3%
5 17759
33.3%
0 17759
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7 17759
33.3%
5 17759
33.3%
0 17759
33.3%

region
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Asia
17759 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters71036
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowAsia
3rd rowAsia
4th rowAsia
5th rowAsia

Common Values

ValueCountFrequency (%)
Asia 17759
100.0%

Length

2025-04-14T14:58:03.566527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:58:03.620597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
asia 17759
100.0%

Most occurring characters

ValueCountFrequency (%)
A 17759
25.0%
s 17759
25.0%
i 17759
25.0%
a 17759
25.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53277
75.0%
Uppercase Letter 17759
 
25.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 17759
33.3%
i 17759
33.3%
a 17759
33.3%
Uppercase Letter
ValueCountFrequency (%)
A 17759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 71036
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 17759
25.0%
s 17759
25.0%
i 17759
25.0%
a 17759
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71036
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 17759
25.0%
s 17759
25.0%
i 17759
25.0%
a 17759
25.0%

event_clarity
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1.0
17118 
2.0
 
641

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters53277
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 17118
96.4%
2.0 641
 
3.6%

Length

2025-04-14T14:58:03.694773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:58:03.758294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 17118
96.4%
2.0 641
 
3.6%

Most occurring characters

ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 17118
32.1%
2 641
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35518
66.7%
Other Punctuation 17759
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17759
50.0%
1 17118
48.2%
2 641
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 17759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 17118
32.1%
2 641
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 17118
32.1%
2 641
 
1.2%

date_prec
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.3 KiB
1.0
15775 
2.0
1640 
4.0
 
164
5.0
 
130
3.0
 
50

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters53277
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row5.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 15775
88.8%
2.0 1640
 
9.2%
4.0 164
 
0.9%
5.0 130
 
0.7%
3.0 50
 
0.3%

Length

2025-04-14T14:58:03.850871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-14T14:58:03.930671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 15775
88.8%
2.0 1640
 
9.2%
4.0 164
 
0.9%
5.0 130
 
0.7%
3.0 50
 
0.3%

Most occurring characters

ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 15775
29.6%
2 1640
 
3.1%
4 164
 
0.3%
5 130
 
0.2%
3 50
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35518
66.7%
Other Punctuation 17759
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17759
50.0%
1 15775
44.4%
2 1640
 
4.6%
4 164
 
0.5%
5 130
 
0.4%
3 50
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 17759
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53277
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 15775
29.6%
2 1640
 
3.1%
4 164
 
0.3%
5 130
 
0.2%
3 50
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53277
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 17759
33.3%
0 17759
33.3%
1 15775
29.6%
2 1640
 
3.1%
4 164
 
0.3%
5 130
 
0.2%
3 50
 
0.1%
Distinct7863
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Memory size277.5 KiB
Minimum1989-01-01 00:00:00
Maximum2023-12-29 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-14T14:58:04.072568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:58:04.218397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct7893
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size277.5 KiB
Minimum1989-01-01 00:00:00
Maximum2023-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-04-14T14:58:04.356609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:58:04.506638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

deaths_a
Real number (ℝ)

Zeros 

Distinct43
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25224154
Minimum0
Maximum6.1654179
Zeros13506
Zeros (%)76.1%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:04.884098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.3862944
Maximum6.1654179
Range6.1654179
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.52688833
Coefficient of variation (CV)2.0888246
Kurtosis9.8104778
Mean0.25224154
Median Absolute Deviation (MAD)0
Skewness2.6677902
Sum4479.5575
Variance0.27761132
MonotonicityNot monotonic
2025-04-14T14:58:05.477490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 13506
76.1%
0.6931471806 2472
 
13.9%
1.098612289 695
 
3.9%
1.386294361 348
 
2.0%
1.609437912 208
 
1.2%
1.791759469 141
 
0.8%
1.945910149 95
 
0.5%
2.079441542 71
 
0.4%
2.197224577 39
 
0.2%
2.397895273 36
 
0.2%
Other values (33) 148
 
0.8%
ValueCountFrequency (%)
0 13506
76.1%
0.6931471806 2472
 
13.9%
1.098612289 695
 
3.9%
1.386294361 348
 
2.0%
1.609437912 208
 
1.2%
1.791759469 141
 
0.8%
1.945910149 95
 
0.5%
2.079441542 71
 
0.4%
2.197224577 39
 
0.2%
2.302585093 25
 
0.1%
ValueCountFrequency (%)
6.165417854 1
< 0.1%
6.056784013 1
< 0.1%
5.993961427 1
< 0.1%
5.313205979 1
< 0.1%
5.153291594 1
< 0.1%
5.087596335 1
< 0.1%
4.343805422 1
< 0.1%
4.025351691 1
< 0.1%
3.988984047 1
< 0.1%
3.828641396 1
< 0.1%

deaths_b
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.51053924
Minimum0
Maximum7.6093665
Zeros9489
Zeros (%)53.4%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:05.727197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.0986123
95-th percentile1.6094379
Maximum7.6093665
Range7.6093665
Interquartile range (IQR)1.0986123

Descriptive statistics

Standard deviation0.64486267
Coefficient of variation (CV)1.2631011
Kurtosis4.343176
Mean0.51053924
Median Absolute Deviation (MAD)0
Skewness1.4143597
Sum9066.6664
Variance0.41584787
MonotonicityNot monotonic
2025-04-14T14:58:05.994924image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 9489
53.4%
0.6931471806 3624
 
20.4%
1.098612289 2260
 
12.7%
1.386294361 1042
 
5.9%
1.609437912 495
 
2.8%
1.791759469 305
 
1.7%
1.945910149 173
 
1.0%
2.079441542 82
 
0.5%
2.197224577 70
 
0.4%
2.302585093 42
 
0.2%
Other values (39) 177
 
1.0%
ValueCountFrequency (%)
0 9489
53.4%
0.6931471806 3624
 
20.4%
1.098612289 2260
 
12.7%
1.386294361 1042
 
5.9%
1.609437912 495
 
2.8%
1.791759469 305
 
1.7%
1.945910149 173
 
1.0%
2.079441542 82
 
0.5%
2.197224577 70
 
0.4%
2.302585093 42
 
0.2%
ValueCountFrequency (%)
7.609366538 1
< 0.1%
7.470224136 1
< 0.1%
7.114769448 1
< 0.1%
6.452048954 1
< 0.1%
5.552959585 1
< 0.1%
5.501258211 1
< 0.1%
5.468060141 1
< 0.1%
5.424950017 1
< 0.1%
5.361292166 1
< 0.1%
5.049856007 1
< 0.1%

deaths_civilians
Real number (ℝ)

High correlation  Zeros 

Distinct63
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30762946
Minimum0
Maximum5.805135
Zeros12408
Zeros (%)69.9%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:06.137031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.69314718
95-th percentile1.3862944
Maximum5.805135
Range5.805135
Interquartile range (IQR)0.69314718

Descriptive statistics

Standard deviation0.56641955
Coefficient of variation (CV)1.8412396
Kurtosis8.6217787
Mean0.30762946
Median Absolute Deviation (MAD)0
Skewness2.4971664
Sum5463.1917
Variance0.32083111
MonotonicityNot monotonic
2025-04-14T14:58:06.263897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12408
69.9%
0.6931471806 3389
 
19.1%
1.098612289 805
 
4.5%
1.386294361 359
 
2.0%
1.609437912 210
 
1.2%
1.791759469 130
 
0.7%
1.945910149 80
 
0.5%
2.079441542 64
 
0.4%
2.197224577 62
 
0.3%
2.302585093 31
 
0.2%
Other values (53) 221
 
1.2%
ValueCountFrequency (%)
0 12408
69.9%
0.6931471806 3389
 
19.1%
1.098612289 805
 
4.5%
1.386294361 359
 
2.0%
1.609437912 210
 
1.2%
1.791759469 130
 
0.7%
1.945910149 80
 
0.5%
2.079441542 64
 
0.4%
2.197224577 62
 
0.3%
2.302585093 31
 
0.2%
ValueCountFrequency (%)
5.805134969 1
< 0.1%
5.549076085 1
< 0.1%
5.209486153 1
< 0.1%
4.955827058 1
< 0.1%
4.94875989 1
< 0.1%
4.477336814 1
< 0.1%
4.394449155 1
< 0.1%
4.343805422 1
< 0.1%
4.317488114 1
< 0.1%
4.248495242 1
< 0.1%

deaths_unknown
Real number (ℝ)

Zeros 

Distinct54
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050611337
Minimum0
Maximum6.7990559
Zeros17127
Zeros (%)96.4%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:06.377609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6.7990559
Range6.7990559
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.32216394
Coefficient of variation (CV)6.3654501
Kurtosis96.967611
Mean0.050611337
Median Absolute Deviation (MAD)0
Skewness8.8242281
Sum898.80673
Variance0.1037896
MonotonicityNot monotonic
2025-04-14T14:58:06.505861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17127
96.4%
0.6931471806 277
 
1.6%
1.098612289 90
 
0.5%
1.386294361 53
 
0.3%
1.609437912 38
 
0.2%
1.791759469 30
 
0.2%
2.079441542 23
 
0.1%
1.945910149 15
 
0.1%
2.397895273 10
 
0.1%
2.564949357 9
 
0.1%
Other values (44) 87
 
0.5%
ValueCountFrequency (%)
0 17127
96.4%
0.6931471806 277
 
1.6%
1.098612289 90
 
0.5%
1.386294361 53
 
0.3%
1.609437912 38
 
0.2%
1.791759469 30
 
0.2%
1.945910149 15
 
0.1%
2.079441542 23
 
0.1%
2.197224577 9
 
0.1%
2.302585093 2
 
< 0.1%
ValueCountFrequency (%)
6.799055862 1
< 0.1%
5.416100402 1
< 0.1%
5.347107531 1
< 0.1%
5.252273428 1
< 0.1%
5.111987788 1
< 0.1%
5.036952602 1
< 0.1%
4.976733742 1
< 0.1%
4.9698133 1
< 0.1%
4.795790546 1
< 0.1%
4.787491743 1
< 0.1%

best
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct101
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4047525
Minimum0
Maximum2442
Zeros1049
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:06.629822image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile8
Maximum2442
Range2442
Interquartile range (IQR)2

Descriptive statistics

Standard deviation29.348185
Coefficient of variation (CV)8.6197703
Kurtosis4402.8757
Mean3.4047525
Median Absolute Deviation (MAD)1
Skewness61.868341
Sum60465
Variance861.31595
MonotonicityNot monotonic
2025-04-14T14:58:06.735805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8193
46.1%
2 3486
19.6%
3 1712
 
9.6%
0 1049
 
5.9%
4 934
 
5.3%
5 658
 
3.7%
6 377
 
2.1%
7 297
 
1.7%
8 195
 
1.1%
10 119
 
0.7%
Other values (91) 739
 
4.2%
ValueCountFrequency (%)
0 1049
 
5.9%
1 8193
46.1%
2 3486
19.6%
3 1712
 
9.6%
4 934
 
5.3%
5 658
 
3.7%
6 377
 
2.1%
7 297
 
1.7%
8 195
 
1.1%
9 115
 
0.6%
ValueCountFrequency (%)
2442 1
< 0.1%
1956 1
< 0.1%
1629 1
< 0.1%
896 1
< 0.1%
805 1
< 0.1%
475 1
< 0.1%
331 1
< 0.1%
274 1
< 0.1%
256 1
< 0.1%
244 1
< 0.1%

high
Real number (ℝ)

High correlation  Skewed 

Distinct123
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2376823
Minimum0
Maximum3226
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:06.840719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile10
Maximum3226
Range3226
Interquartile range (IQR)2

Descriptive statistics

Standard deviation39.677917
Coefficient of variation (CV)9.3631174
Kurtosis3778.729
Mean4.2376823
Median Absolute Deviation (MAD)1
Skewness56.211448
Sum75257
Variance1574.3371
MonotonicityNot monotonic
2025-04-14T14:58:06.967601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8514
47.9%
2 3660
20.6%
3 1831
 
10.3%
4 995
 
5.6%
5 712
 
4.0%
6 438
 
2.5%
7 330
 
1.9%
8 226
 
1.3%
10 139
 
0.8%
9 131
 
0.7%
Other values (113) 783
 
4.4%
ValueCountFrequency (%)
0 3
 
< 0.1%
1 8514
47.9%
2 3660
20.6%
3 1831
 
10.3%
4 995
 
5.6%
5 712
 
4.0%
6 438
 
2.5%
7 330
 
1.9%
8 226
 
1.3%
9 131
 
0.7%
ValueCountFrequency (%)
3226 1
< 0.1%
2442 1
< 0.1%
1956 1
< 0.1%
1629 1
< 0.1%
896 1
< 0.1%
805 1
< 0.1%
724 1
< 0.1%
720 1
< 0.1%
600 1
< 0.1%
416 1
< 0.1%

low
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct95
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2853764
Minimum0
Maximum2442
Zeros1463
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size277.5 KiB
2025-04-14T14:58:07.095874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q33
95-th percentile8
Maximum2442
Range2442
Interquartile range (IQR)2

Descriptive statistics

Standard deviation29.197549
Coefficient of variation (CV)8.8871245
Kurtosis4494.5887
Mean3.2853764
Median Absolute Deviation (MAD)1
Skewness62.751819
Sum58345
Variance852.49689
MonotonicityNot monotonic
2025-04-14T14:58:07.226624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 7989
45.0%
2 3412
19.2%
3 1670
 
9.4%
0 1463
 
8.2%
4 921
 
5.2%
5 642
 
3.6%
6 370
 
2.1%
7 285
 
1.6%
8 184
 
1.0%
10 115
 
0.6%
Other values (85) 708
 
4.0%
ValueCountFrequency (%)
0 1463
 
8.2%
1 7989
45.0%
2 3412
19.2%
3 1670
 
9.4%
4 921
 
5.2%
5 642
 
3.6%
6 370
 
2.1%
7 285
 
1.6%
8 184
 
1.0%
9 113
 
0.6%
ValueCountFrequency (%)
2442 1
< 0.1%
1956 1
< 0.1%
1629 1
< 0.1%
896 1
< 0.1%
805 1
< 0.1%
475 1
< 0.1%
274 1
< 0.1%
256 1
< 0.1%
244 1
< 0.1%
236 1
< 0.1%

Interactions

2025-04-14T14:57:48.694679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:41.616826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:45.724994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:50.109240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:55.269638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:59.980380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:04.417169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.510237image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.939538image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:12.282319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:15.599601image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:18.233707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.557552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:23.038409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:26.407711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:29.256592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.730247image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:34.428124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:38.695017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:41.235927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.902196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:46.214269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:48.793463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:41.757814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:45.864995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:50.279026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:55.411850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:00.157448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:04.557580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.608024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:10.045853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:12.382276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:15.745990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:18.326464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.660701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:23.145761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:26.534268image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:29.362002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.832898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:34.533261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:38.827172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:41.335165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.997380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:46.321272image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:48.904832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:41.914809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:46.015025image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:50.475667image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:55.616863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:00.351747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:04.676910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.707150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:10.156450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:12.487193image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:15.909894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:18.430347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.772273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:23.261958image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:26.700080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:29.466424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.951512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:34.640434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:38.961302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:41.443120image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:44.118178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:46.416075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:49.029943image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:42.092242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:46.179826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:50.685914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:55.794227image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:00.522525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:04.769182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.818611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:10.271718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:12.592532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:16.082017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:18.532466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.896859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:23.382068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:26.841930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:29.579528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:32.078939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:34.755518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:39.133859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:41.560433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:44.242777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:46.524340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-14T14:57:19.572511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.022828image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-14T14:57:28.273571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-14T14:56:44.098564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-04-14T14:57:09.058680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:11.431131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:14.473100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:17.288076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:19.681690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.136584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:25.496424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:28.391188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:30.812259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:33.466655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:36.062774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:40.382922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:42.950252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:45.409278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:47.719632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:50.654439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:44.261121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:48.792659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:53.800723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:58.546017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:03.488467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:06.780517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.190348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:11.542860image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:14.593375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:17.409905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:19.786640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.268862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:25.609494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:28.507200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:30.917214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:33.593110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:36.195042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:40.469294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.092864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:45.509182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:47.838948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:50.803265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:44.451874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:48.985275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:54.041610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:58.762797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:03.607772image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:06.880689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.301432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:11.639015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:14.732148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:17.516906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:19.900826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.371489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:25.723942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:28.614058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.032665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:33.712878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:36.299344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:40.568872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.244545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:45.597107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:47.985021image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:50.931886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:44.784488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:49.171020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:54.238097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:58.946815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:03.728264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:06.984100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.406815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:11.741587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:14.888977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:17.632801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.021768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.486588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:25.838779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:28.717014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.141289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:33.822130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:37.886787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:40.673642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.347672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:45.694073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:48.105016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:51.090674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:44.951862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:49.326429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:54.397895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:59.124089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:03.846177image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.096111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.515552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:11.844136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:15.043742image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:17.742068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.134836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.599864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:25.944058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:28.827402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.254264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:33.947164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:37.998855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:40.780194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.459556image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:45.794332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:48.250528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:51.252342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:45.194117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:49.492072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:54.664073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:59.372522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:03.972443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.209316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.623306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:11.949697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:15.182418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:17.878793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.252352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.724740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:26.064482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:28.930994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.359069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:34.080529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:38.214660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:40.892274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.573105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:45.901218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:48.376830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:51.364573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:45.361026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:49.631777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:54.925134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:59.606741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:04.126420image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.309836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.729849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:12.063890image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:15.331225image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:17.990873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.345389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.821603image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:26.179276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:29.044044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.472167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:34.208325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:38.393571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:40.999565image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.677515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:45.989346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:48.483939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:51.445722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:45.504303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:49.911880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:55.110378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:56:59.788855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:04.283697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:07.409387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:09.834634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:12.174436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:15.476382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:18.117340image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:20.444597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:22.931567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:26.289680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:29.139070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:31.599432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:34.314754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:38.555607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:41.112879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:43.790401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:46.106532image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-04-14T14:57:48.590155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-04-14T14:58:07.334441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
active_yearadm_1bestconflict_dset_idconflict_nameconflict_new_iddate_precdeaths_adeaths_bdeaths_civiliansdeaths_unknowndyad_dset_iddyad_namedyad_new_idevent_clarityhighidlatitudelongitudelownumber_of_sourcespriogrid_gidside_aside_a_dset_idside_a_new_idside_bside_b_dset_idside_b_new_idtype_of_violencewhere_prec
active_year1.0000.308-0.215-0.1890.351-0.0890.117-0.058-0.021-0.006-0.074-0.0800.375-0.0780.289-0.2970.290-0.2940.190-0.2130.385-0.3090.266-0.038-0.0380.291-0.073-0.0820.123-0.135
adm_10.3081.0000.0400.3650.4800.3830.1180.0610.0770.1500.1180.3660.4910.3860.1270.0330.1740.7930.6480.0410.0680.7310.3410.2690.2690.4120.3340.3270.4950.275
best-0.2150.0401.0000.1380.023-0.1110.1300.2370.4090.0820.1410.1500.000-0.1130.1150.829-0.0520.060-0.0580.952-0.0290.0570.000-0.124-0.1240.041-0.1350.1490.0110.042
conflict_dset_id-0.1890.3650.1381.0000.9980.2190.0720.0570.354-0.3950.1170.9220.9980.2200.0940.117-0.0390.421-0.1590.138-0.0060.4210.846-0.212-0.2120.8790.0180.8160.642-0.034
conflict_name0.3510.4800.0230.9981.0000.9980.1480.1380.1520.3690.2320.9531.0000.9610.1660.0120.2940.5750.5650.0240.1160.5460.9990.9980.9980.8470.8700.8380.9980.223
conflict_new_id-0.0890.383-0.1110.2190.9981.0000.072-0.328-0.3360.5060.072-0.0740.9980.9860.092-0.068-0.0550.0380.150-0.126-0.0870.0470.8620.8100.8100.7870.920-0.1950.690-0.063
date_prec0.1170.1180.1300.0720.1480.0721.0000.1120.1440.0710.1650.0740.1500.0690.6010.1350.0680.0800.0810.1220.0330.0790.1190.0870.0870.1410.0260.0820.0960.138
deaths_a-0.0580.0610.2370.0570.138-0.3280.1121.000-0.194-0.250-0.0240.1620.147-0.3280.1170.1810.0010.040-0.0410.2390.0130.0460.107-0.304-0.3040.152-0.3660.1820.242-0.030
deaths_b-0.0210.0770.4090.3540.152-0.3360.144-0.1941.000-0.511-0.1140.4530.153-0.3270.2130.315-0.0290.194-0.1780.4090.0400.1790.125-0.515-0.5150.159-0.3600.5080.2680.053
deaths_civilians-0.0060.1500.082-0.3950.3690.5060.071-0.250-0.5111.000-0.085-0.5750.3680.4960.101-0.0020.010-0.1450.1270.052-0.012-0.1370.3630.6590.6590.2570.590-0.6430.547-0.061
deaths_unknown-0.0740.1180.1410.1170.2320.0720.165-0.024-0.114-0.0851.0000.1270.2310.0690.2340.1270.008-0.0530.0150.129-0.022-0.0470.2110.0930.0930.215-0.0030.1280.250-0.009
dyad_dset_id-0.0800.3660.1500.9220.953-0.0740.0740.1620.453-0.5750.1271.0000.998-0.0500.0930.110-0.0170.309-0.1190.1540.0310.3050.767-0.461-0.4610.880-0.2600.9220.653-0.028
dyad_name0.3750.4910.0000.9981.0000.9980.1500.1470.1530.3680.2310.9981.0000.9980.1670.0000.3040.5870.5710.0000.1190.5570.9990.9980.9980.9990.9980.9980.9980.228
dyad_new_id-0.0780.386-0.1130.2200.9610.9860.069-0.328-0.3270.4960.069-0.0500.9981.0000.091-0.071-0.0560.0270.162-0.128-0.0830.0360.8420.7960.7960.7470.923-0.1700.673-0.060
event_clarity0.2890.1270.1150.0940.1660.0920.6010.1170.2130.1010.2340.0930.1670.0911.0000.1300.0750.0690.1060.1080.0430.0720.1370.0940.0940.1600.0370.1000.0940.332
high-0.2970.0330.8290.1170.012-0.0680.1350.1810.315-0.0020.1270.1100.000-0.0710.1301.000-0.0800.074-0.0410.786-0.0910.0750.000-0.072-0.0720.032-0.0950.1040.0190.116
id0.2900.174-0.052-0.0390.294-0.0550.0680.001-0.0290.0100.008-0.0170.304-0.0560.075-0.0801.000-0.010-0.072-0.0460.412-0.0140.214-0.017-0.0170.258-0.055-0.0170.246-0.072
latitude-0.2940.7930.0600.4210.5750.0380.0800.0400.194-0.145-0.0530.3090.5870.0270.0690.074-0.0101.000-0.7190.0720.0950.9830.383-0.183-0.1830.4890.0290.3210.308-0.052
longitude0.1900.648-0.058-0.1590.5650.1500.081-0.041-0.1780.1270.015-0.1190.5710.1620.106-0.041-0.072-0.7191.000-0.081-0.214-0.6800.3770.2170.2170.4780.103-0.1730.2930.118
low-0.2130.0410.9520.1380.024-0.1260.1220.2390.4090.0520.1290.1540.000-0.1280.1080.786-0.0460.072-0.0811.000-0.0330.0680.000-0.142-0.1420.042-0.1500.1650.0100.045
number_of_sources0.3850.068-0.029-0.0060.116-0.0870.0330.0130.040-0.012-0.0220.0310.119-0.0830.043-0.0910.4120.095-0.214-0.0331.0000.0840.109-0.090-0.0900.069-0.0780.0350.038-0.104
priogrid_gid-0.3090.7310.0570.4210.5460.0470.0790.0460.179-0.137-0.0470.3050.5570.0360.0720.075-0.0140.983-0.6800.0680.0841.0000.356-0.176-0.1760.4640.0380.3170.289-0.060
side_a0.2660.3410.0000.8460.9990.8620.1190.1070.1250.3630.2110.7670.9990.8420.1370.0000.2140.3830.3770.0000.1090.3561.0000.9990.9990.5770.6280.6390.9710.162
side_a_dset_id-0.0380.269-0.124-0.2120.9980.8100.087-0.304-0.5150.6590.093-0.4610.9980.7960.094-0.072-0.017-0.1830.217-0.142-0.090-0.1760.9991.0001.0000.7960.757-0.6140.430-0.033
side_a_new_id-0.0380.269-0.124-0.2120.9980.8100.087-0.304-0.5150.6590.093-0.4610.9980.7960.094-0.072-0.017-0.1830.217-0.142-0.090-0.1760.9991.0001.0000.7960.757-0.6140.430-0.033
side_b0.2910.4120.0410.8790.8470.7870.1410.1520.1590.2570.2150.8800.9990.7470.1600.0320.2580.4890.4780.0420.0690.4640.5770.7960.7961.0000.9990.9990.9960.181
side_b_dset_id-0.0730.334-0.1350.0180.8700.9200.026-0.366-0.3600.590-0.003-0.2600.9980.9230.037-0.095-0.0550.0290.103-0.150-0.0780.0380.6280.7570.7570.9991.000-0.3010.744-0.046
side_b_new_id-0.0820.3270.1490.8160.838-0.1950.0820.1820.508-0.6430.1280.9220.998-0.1700.1000.104-0.0170.321-0.1730.1650.0350.3170.639-0.614-0.6140.999-0.3011.0000.499-0.018
type_of_violence0.1230.4950.0110.6420.9980.6900.0960.2420.2680.5470.2500.6530.9980.6730.0940.0190.2460.3080.2930.0100.0380.2890.9710.4300.4300.9960.7440.4991.0000.134
where_prec-0.1350.2750.042-0.0340.223-0.0630.138-0.0300.053-0.061-0.009-0.0280.228-0.0600.3320.116-0.072-0.0520.1180.045-0.104-0.0600.162-0.033-0.0330.181-0.046-0.0180.1341.000

Missing values

2025-04-14T14:57:51.663999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-14T14:57:51.982865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idrelidyearactive_yearcode_statustype_of_violenceconflict_dset_idconflict_new_idconflict_namedyad_dset_iddyad_new_iddyad_nameside_a_dset_idside_a_new_idside_aside_b_dset_idside_b_new_idside_bnumber_of_sourcessource_articlesource_originalwhere_precwhere_coordinateswhere_descriptionadm_1adm_2latitudelongitudegeom_wktpriogrid_gidcountryiso3country_idregionevent_claritydate_precdate_startdate_enddeaths_adeaths_bdeaths_civiliansdeaths_unknownbesthighlow
171629IND-1989-1-345-019891989Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1AGGREGATIONAGGREGATION EVENT5.0LoCIndia - Pakistan borderUnknownUnknown32.5650074.67600POINT (74.676 32.565)176910IndiaIND750Asia1.05.01989-01-011989-12-310.0000000.0000000.03.258097252525
271630IND-1990-1-345-019901990Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1AGGREGATIONAGGREGATION EVENT5.0LoCIndia - Pakistan borderUnknownUnknown32.5650074.67600POINT (74.676 32.565)176910IndiaIND750Asia2.05.01990-01-011990-12-310.0000000.0000000.02.89037217617
359216PAK-1990-1-345-119901990Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1Reuters 13/4 1990United News of India5.0LoCKashmir no-man's-landUnknownUnknown32.5650074.67600POINT (74.676 32.565)176910IndiaIND750Asia1.01.01990-04-131990-04-130.6931471.7917590.00.000000666
459218PAK-1990-1-345-219901990Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1Reuters 21/4 1990, 22/4 1990Press Trust of India3.0Poonch districtBaglairdaraJammu and Kashmir statePoonch district33.7700074.10000POINT (74.1 33.77)178349IndiaIND750Asia1.01.01990-04-211990-04-210.0000000.0000000.00.000000040
559247PAK-1990-1-345-319901990Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1Reuters 6/5 1990Indian officials5.0LoCKashmir no-man's-landUnknownUnknown32.5650074.67600POINT (74.676 32.565)176910IndiaIND750Asia1.01.01990-05-061990-05-060.0000000.0000000.00.000000020
659413PAK-1990-1-345-419901990Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1Reuters 14/5 1990, 15/5 1990Press Trust of India / Pakistan army spokesman Brigadier Riaz Ullah3.0Poonch districtPunch districtJammu and Kashmir statePoonch district33.7700074.10000POINT (74.1 33.77)178349IndiaIND750Asia1.01.01990-05-141990-05-140.0000001.0986120.00.000000270
759506PAK-1991-1-345-319911991Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1Reuters 18/5 1991, The Independent 6/6 1991 "Kashmiris flee as summerbrings warwith Indian troops on borderPakistani newspapers4.0Jammu and Kashmir stateKashmirJammu and Kashmir stateUnknown33.9166776.66667POINT (76.66667 33.91667)178354IndiaIND750Asia2.05.01991-06-051991-11-010.0000000.0000000.00.0000000900
8372975IND-1991-1-422-119911991Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan1"Reuters News,1991-06-09,INDIAN SOLDIER KILLED IN CLASH WITH PAKISTANI TROOPS."an Indian defence ministry spokesman4.0Jammu and Kashmir stateKashmir regionJammu and Kashmir stateUnknown33.9166776.66667POINT (76.66667 33.91667)178354IndiaIND750Asia1.01.01991-06-071991-06-070.6931470.0000000.00.000000111
954312PAK-1991-1-345-1019911991Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1Reuters 27/8 1991Indian army sources4.0Jammu and Kashmir stateKashmirJammu and Kashmir stateUnknown33.9166776.66667POINT (76.66667 33.91667)178354IndiaIND750Asia2.02.01991-08-231991-08-271.0986120.0000000.00.000000222
1054839PAK-1991-1-345-219911991Clear1.0218218India - Pakistan422422Government of India - Government of Pakistan141141Government of India142142Government of Pakistan-1Agence Europe 4/9 1991 "Border Cease-fire Agreement Signed with PakistanUnknown4.0Jammu and Kashmir stateKashmirJammu and Kashmir stateUnknown33.9166776.66667POINT (76.66667 33.91667)178354IndiaIND750Asia2.04.01991-08-261991-09-030.0000000.0000000.03.931826505050
idrelidyearactive_yearcode_statustype_of_violenceconflict_dset_idconflict_new_idconflict_namedyad_dset_iddyad_new_iddyad_nameside_a_dset_idside_a_new_idside_aside_b_dset_idside_b_new_idside_bnumber_of_sourcessource_articlesource_originalwhere_precwhere_coordinateswhere_descriptionadm_1adm_2latitudelongitudegeom_wktpriogrid_gidcountryiso3country_idregionevent_claritydate_precdate_startdate_enddeaths_adeaths_bdeaths_civiliansdeaths_unknownbesthighlow
17750422047IND-2021-1-14685-520212021Clear1.01365313653India: Western South East Asia1468514685Government of India - UNLFW141141Government of India63206320UNLFW2"Press Trust of India,2021-11-15,Three NSCN-K rebels shot dead by Assam Rifles in Arunachal";"SATP,2021-11-15,On November 15, three militants belonging to the Yung Aung faction of the National Socialist Council of Nagaland-Khaplang (NSCN-KYA) were killed in an encounter in Longding District of Arunachal Pradesh."Longding Deputy Commissioner Bani Lego1.0Khogla villageKhogla Village in Wakka Circle, Arunachal Pradesh’s Longding district.Arunachal Pradesh stateLongding district26.84676295.450524POINT (95.450524 26.846762)168311IndiaIND750Asia1.01.02021-11-152021-11-150.0000001.3862940.0000000.000000333
17751424179IND-2021-1-14685-720212021Clear1.01365313653India: Western South East Asia1468514685Government of India - UNLFW141141Government of India63206320UNLFW3"Assam Tribune,2021-12-05,Security forces kill 14 civilians in Nagaland";"The Pioneer,2021-12-06,14 civilians killed in Nagaland";"The Telegraph,2021-12-07,Amit Shah repents loss of lives in Nagaland killings"police; Home Minister Amit Shah2.0Otting villagea place between Tiru and Oting villages under Tizit subdivision of Mon district of NagalandNagaland stateMon district26.84478594.957102POINT (94.957102 26.844785)168310IndiaIND750Asia1.01.02021-12-042021-12-040.0000000.0000002.1972250.000000886
17752427729IND-2022-1-14685-020222022Clear1.01365313653India: Western South East Asia1468514685Government of India - UNLFW141141Government of India63206320UNLFW3"Press Trust of India,2022-01-05,Assam Rifles jawan killed, another injured in bomb attack";"SATP,2022-01-05,On January 5, one jawan identified as Rifleman L Wangshu of 16 Assam Rifles (AR) was killed in an Improvised Explosive device (IED) blast";"Crisis Watch,2022-01-31,India JANUARY 2022"police official; PREPAK-pro2.0Thoubal townnear a reservoir of the Sangomsang Water Supply works, Waithou Sangomsang in Thoubal district\n/ Lilong Ushoipokpi Sangomsang along the Imphal-Moreh RoadManipur stateThoubal district24.64327093.997412POINT (93.997412 24.64327)165428IndiaIND750Asia1.01.02022-01-052022-01-050.6931470.0000000.0000000.000000111
17753443950IND-2022-1-14685-120222022Clear1.01365313653India: Western South East Asia1468514685Government of India - UNLFW141141Government of India63206320UNLFW1"SATP,2022-07-01,On July 1, one United Liberation Front of Asom-Independent (ULFA-I) militant was killed in an ongoing encounter in Kakopathar area"officials2.0Kakopathar townKakopathar area of Tinsukia District in AssamAssam stateTinsukia district27.63979595.671135POINT (95.671135 27.639795)169752IndiaIND750Asia1.01.02022-07-012022-07-010.0000000.6931470.0000000.000000111
17754466230IND-2023-1-14685-020232023Clear1.01365313653India: Western South East Asia1468514685Government of India - UNLFW141141Government of India63206320UNLFW2"Press Trust of India,2023-02-09,ULFA(I) militant killed in encounter in Assam";"SATP,2023-02-10,February - 9"Police spokesman2.0Malu Gaon village, Margherita sub-districtMalugaon area in Assam’s Tinsukia district \nTikak Mulung Parbat near Margherita town in Tinsukia District of AssamAssam stateTinsukia district27.28393995.744168POINT (95.744168 27.283939)169032IndiaIND750Asia1.01.02023-02-092023-02-090.0000000.6931470.0000000.000000111
17755474473IND-2023-1-14685-520232023Clear1.01365313653India: Western South East Asia1468514685Government of India - UNLFW141141Government of India63206320UNLFW2"Press Trust of India,2023-04-24,Two KLO militants killed, 4 held in Assam: Police";"SATP,2023-04-24,April - 24"Assam Police2.0Chakrashila Hill forest reserve, Kokrajhar districtin Chakrashila hill and surrounding jungle in Assam's Kokrajhar districtAssam stateKokrajhar district26.33152690.339379POINT (90.339379 26.331526)167581IndiaIND750Asia1.01.02023-04-242023-04-240.0000001.0986120.0000000.000000222
17756487548IND-2023-1-14685-620232023Clear1.01365313653India: Western South East Asia1468514685Government of India - UNLFW141141Government of India63206320UNLFW1"Press Trust of India,2023-08-10,Hardcore NSCN-K operative killed, one arrested in Arunachal"senior police official, Tirop SP Rahul Gupta2.0Hukanjuri Villagean area close to Hukanjuri in Arunachal Pradesh’s Tirap districtArunachal Pradesh stateTirap district27.11061695.463553POINT (95.463553 27.110616)169031IndiaIND750Asia1.01.02023-08-102023-08-100.0000000.6931470.0000000.000000111
1775795591IND-1993-2-15181-019931993Clear2.01518114003Meitei - Pangal1518115181Meitei - Pangal29072907Meitei67796779Pangal2"New York Times ,1993-05-04,Hindu-Muslim Rioting Is Fatal to 25 in India";"South Asia Terrorism Portal,2017-09-19,People's United Liberation Front"said Manipur state police chief Alfred Liddle3.0Imphal districtnortheast Indian state of ManipurManipur stateImphal district24.96666793.550000POINT (93.55 24.966667)165428IndiaIND750Asia1.01.01993-05-031993-05-030.0000000.0000000.0000003.258097254025
1775895592IND-1993-2-15181-119931993Clear2.01518114003Meitei - Pangal1518115181Meitei - Pangal29072907Meitei67796779Pangal2"New York Times ,1993-05-04,Hindu-Muslim Rioting Is Fatal to 25 in India";"South Asia Terrorism Portal,2017-09-19,People's United Liberation Front"police3.0Imphal districtManipurManipur stateImphal district24.96666793.550000POINT (93.55 24.966667)165428IndiaIND750Asia1.01.01993-05-041993-05-040.0000000.0000000.0000004.454347858585
1775995595IND-1993-2-15181-219931993Clear2.01518114003Meitei - Pangal1518115181Meitei - Pangal29072907Meitei67796779Pangal2"New York Times ,1993-05-04,Hindu-Muslim Rioting Is Fatal to 25 in India";"South Asia Terrorism Portal,2017-09-19,People's United Liberation Front"police3.0Imphal districtManipurManipur stateImphal district24.96666793.550000POINT (93.55 24.966667)165428IndiaIND750Asia1.01.01993-05-061993-05-060.0000000.0000000.0000002.995732191919